database_export.sql

PRAGMA foreign_keys=OFF;
BEGIN TRANSACTION;
CREATE TABLE projects (
        id TEXT PRIMARY KEY,
        name TEXT NOT NULL,
        description TEXT,
        status TEXT DEFAULT 'active',
        created_at TEXT DEFAULT (datetime('now')),
        updated_at TEXT DEFAULT (datetime('now'))
      );
INSERT INTO projects VALUES('cf040508-efa2-441c-936a-33fb21cf3c5d','ML Terminal Time Prediction - Terneuzen','Testing a machine learning model to predict ship terminal processing times at our Terneuzen facility. This project involves working with the customer service team and building monitoring dashboards.','active','2025-08-17 20:37:57','2025-09-30 08:04:43');
INSERT INTO projects VALUES('f5a28680-50e5-467c-9dd8-6b7f69e7202c','Continuous Improvement platform ','To create an integral continuous improvement platform were users are guided through all DMAIC steps and phases','active','2025-08-28 06:36:30','2025-08-28 06:36:30');
INSERT INTO projects VALUES('546087b3-af9e-4429-8119-ef58d0471290','Consolidation Vessel Clearance','Consolidation and automation of different information streams regarding Legal and CS (Vessel data/sanction data)','active','2025-10-02 07:19:18','2025-10-02 07:19:18');
INSERT INTO projects VALUES('7054d2a2-2de6-4956-b31f-947cf7d58b8b','Circulation Invoicing automation','Automating the invoicing of circulation from log trigger in TMS','active','2025-10-02 07:20:06','2025-10-02 07:20:06');
INSERT INTO projects VALUES('33e4980d-faba-4b2d-9b55-591f7a7c5431','Newsletters (AI & CI)','Create automations for generation of newsletters on AI and CI','active','2025-10-02 07:21:30','2025-10-02 07:21:30');
INSERT INTO projects VALUES('f21bc828-fac6-4175-9e4c-d28b9e18ba50','KPI hierarchy from PowerBI and TMS','Derive useful insights from data in powerbi and TMS (Sol)','active','2025-10-02 07:22:29','2025-10-02 07:22:29');
CREATE TABLE notes (
        id TEXT PRIMARY KEY,
        project_id TEXT REFERENCES projects(id) ON DELETE CASCADE,
        content TEXT NOT NULL,
        enhanced_content TEXT,
        structured_data TEXT,
        created_at TEXT DEFAULT (datetime('now')),
        updated_at TEXT DEFAULT (datetime('now'))
      );
INSERT INTO notes VALUES('39086721-a884-4690-93c0-77d792d3670d','cf040508-efa2-441c-936a-33fb21cf3c5d','Got an executable from the latest version of the model trainer and prediction part, on GitHub, ready to distribute',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Project Update\u000aSuccessfully obtained the executable from the latest version of the machine learning model trainer and prediction component. The executable is now available on GitHub and ready for distribution to relevant stakeholders and implementation teams.\u000a\u000a## Technical Status\u000a- Latest version of ML model trainer extracted as executable\u000a- Prediction component ready for deployment\u000a- Code repository updated on GitHub\u000a- Distribution package prepared for rollout'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Project Update\nSuccessfully obtained the executable from the latest version of the machine learning model trainer and prediction component. The executable is now available on GitHub and ready for distribution to relevant stakeholders and implementation teams.\n\n## Technical Status\n- Latest version of ML model trainer extracted as executable\n- Prediction component ready for deployment\n- Code repository updated on GitHub\n- Distribution package prepared for rollout","key_insights":["Executable version of ML model trainer is now available","Prediction component is ready for deployment","Code is properly stored in GitHub repository","Distribution package is prepared and ready for rollout"],"action_items":["Distribute the executable to implementation team","Schedule training session on using the new model","Update project documentation with latest version details","Notify customer service team about the availability of the new prediction tool"],"risks":["Potential compatibility issues with existing systems at Terneuzen facility","User adoption challenges if training is not provided","Data quality issues affecting prediction accuracy","Possible integration challenges with monitoring dashboards"],"opportunities":["Improve terminal processing time predictions at Terneuzen facility","Enhance customer service team''s planning capabilities","Create benchmark for prediction accuracy to measure improvement","Leverage successful implementation for other Evos locations"],"stakeholders":["Customer Service Team","IT Department","Operations Team at Terneuzen","Data Science/ML Development Team","Project Management"],"next_steps":["Coordinate distribution plan with IT department","Set up validation testing at Terneuzen facility","Prepare user documentation and training materials","Schedule implementation review meeting with stakeholders","Configure monitoring dashboards to track model performance"]}','2025-08-18 06:50:00','2025-08-18 06:50:00');
INSERT INTO notes VALUES('184a7bb3-a4d1-4d51-b62a-1af76e032602','cf040508-efa2-441c-936a-33fb21cf3c5d','Worked on a vessel finder api integration but it''s hard to get an account, have to go through sales. Its better to check for existing accounts',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## API Integration Progress\u000aInvestigated Vessel Finder API integration for the terminal time prediction model. Encountered challenges with account creation as it requires going through their sales process. Recommended action is to check for existing Evos accounts before pursuing new account setup.\u000a\u000a## Project Status\u000aCurrently working on data integration components to support the machine learning model that will predict ship processing times at the Terneuzen terminal. This initiative will provide valuable insights for the customer service team through monitoring dashboards.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## API Integration Progress\nInvestigated Vessel Finder API integration for the terminal time prediction model. Encountered challenges with account creation as it requires going through their sales process. Recommended action is to check for existing Evos accounts before pursuing new account setup.\n\n## Project Status\nCurrently working on data integration components to support the machine learning model that will predict ship processing times at the Terneuzen terminal. This initiative will provide valuable insights for the customer service team through monitoring dashboards.","key_insights":["Vessel Finder API requires a formal sales process for account creation","Existing organizational accounts may already have access to required vessel data","API integration is a critical component for accurate terminal time predictions"],"action_items":["Check with IT department for existing Vessel Finder accounts or similar services","Document API requirements and alternatives if Vessel Finder is not viable","Align with customer service team on data needs for the monitoring dashboard"],"risks":["Delayed API integration may impact project timeline","Potential additional costs if new API subscription is required","Data quality or availability issues from third-party sources"],"opportunities":["Leveraging existing company accounts could accelerate implementation","Standardizing vessel data collection across terminals","Improving customer service response times with predictive insights"],"stakeholders":["IT Department","Customer Service Team","Terneuzen Terminal Operations","Procurement/Purchasing (for potential API subscription)"],"next_steps":["Create inventory of existing data service subscriptions across Evos","Develop requirements document for vessel tracking data needs","Schedule alignment meeting with customer service team on dashboard requirements","Investigate alternative vessel tracking data sources as contingency"]}','2025-08-18 06:58:32','2025-08-18 06:58:32');
INSERT INTO notes VALUES('f6d223c9-649c-4422-a9d7-377eca3eaeaf','cf040508-efa2-441c-936a-33fb21cf3c5d','First thing when in Terneuzen is log into OpenTAS TMS and get an export. This export is used to make the the training input adaptable to it, and create a feedback loop to measure model performance',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Data Collection Process\u000aWhen arriving at the Terneuzen facility, the first priority is to log into the OpenTAS Terminal Management System (TMS) and extract the necessary data export. This export serves two critical purposes:\u000a\u000a1. Adapting the training input for the machine learning model\u000a2. Establishing a feedback loop to effectively measure and validate model performance\u000a\u000aThis systematic data collection process is essential for the ongoing development and refinement of our ship terminal processing time prediction model.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Data Collection Process\nWhen arriving at the Terneuzen facility, the first priority is to log into the OpenTAS Terminal Management System (TMS) and extract the necessary data export. This export serves two critical purposes:\n\n1. Adapting the training input for the machine learning model\n2. Establishing a feedback loop to effectively measure and validate model performance\n\nThis systematic data collection process is essential for the ongoing development and refinement of our ship terminal processing time prediction model.","key_insights":["OpenTAS TMS data export is the foundation for model training and validation","A feedback loop has been established to measure prediction accuracy","The data collection process must be performed on-site at Terneuzen","The export requires adaptation to be compatible with the ML training input"],"action_items":["Create a standardized procedure for OpenTAS TMS data extraction","Develop an automated data transformation pipeline for the training input","Implement metrics to quantify model performance using the feedback loop","Document the data collection and processing methodology"],"risks":["Data quality issues in OpenTAS TMS could affect model accuracy","Manual export process may lead to inconsistent data collection","Without proper feedback mechanisms, model drift may go undetected","Access limitations to OpenTAS TMS could disrupt the workflow"],"opportunities":["Automate the data export process to reduce manual effort","Expand the model to include additional variables from OpenTAS TMS","Create visualization tools to monitor prediction accuracy over time","Develop a knowledge transfer protocol to enable team members to perform the process"],"stakeholders":["Terneuzen facility operations team","Customer service team","Data science/ML development team","OpenTAS TMS administrators","Dashboard users/monitors"],"next_steps":["Schedule regular data collection visits to Terneuzen","Establish performance benchmarks for the prediction model","Meet with customer service team to review initial results","Begin development of monitoring dashboards","Document the complete data flow from extraction to model output"]}','2025-08-18 07:02:26','2025-08-18 07:02:26');
INSERT INTO notes VALUES('1685f590-1f71-40ae-a2ec-bfbc92cdfcbf','cf040508-efa2-441c-936a-33fb21cf3c5d','Started up in Terneuzen, got my OpenTAS account working and some explanation on how to find data in the system. There were some issues with the ML app after making some updates and adding some features. I''ve been going through the bugs and I''m nearly ready. Then I''ll start going through historical data in TMS with regards to estimated arrival and departure times. First impression is that I need to through every order separately, because the export doesn''t include these values.',unistr('## ML Terminal Time Prediction Project - Terneuzen Update\u000a\u000aSuccessfully initiated project work at the Terneuzen facility. Completed OpenTAS account setup and received orientation on data location and extraction procedures within the system.\u000a\u000aEncountered technical challenges with the Machine Learning application following recent feature additions and updates. Have systematically addressed most bugs and the application is approaching deployment readiness.\u000a\u000aNext phase involves comprehensive analysis of historical data in the Terminal Management System (TMS), specifically focusing on estimated arrival and departure time accuracy. Initial assessment indicates that individual order review will be necessary, as the standard export functionality does not include these critical time prediction values.\u000a\u000a'),'{"enhanced_content":"## ML Terminal Time Prediction Project - Terneuzen Update\n\nSuccessfully initiated project work at the Terneuzen facility. Completed OpenTAS account setup and received orientation on data location and extraction procedures within the system.\n\nEncountered technical challenges with the Machine Learning application following recent feature additions and updates. Have systematically addressed most bugs and the application is approaching deployment readiness.\n\nNext phase involves comprehensive analysis of historical data in the Terminal Management System (TMS), specifically focusing on estimated arrival and departure time accuracy. Initial assessment indicates that individual order review will be necessary, as the standard export functionality does not include these critical time prediction values.\n\n","key_insights":["OpenTAS account is now operational, enabling direct system access","ML application experienced bugs after updates and feature additions","Standard TMS exports lack estimated arrival/departure times","Individual order review appears necessary for complete data collection","Bug fixes for the ML application are nearly complete"],"action_items":["Complete remaining ML application bug fixes","Develop methodology for efficient individual order review in TMS","Create data collection template for estimated vs. actual arrival/departure times","Schedule meeting with TMS administrator to explore alternative data extraction options","Document OpenTAS data extraction process for future reference"],"risks":["Manual review of individual orders may be time-intensive and delay project timeline","ML model accuracy may be compromised if historical data is incomplete","System access or permission issues could impede data collection","Undocumented bugs may emerge after initial fixes are implemented"],"opportunities":["Improve standard TMS export functionality to include time prediction data","Develop automated data extraction solution to eliminate manual review","Enhance ML model with additional parameters from OpenTAS","Create reusable documentation for future system integration projects"],"stakeholders":["Customer Service team","TMS administrators","OpenTAS system owners","ML application development team","Terminal operations staff at Terneuzen"],"next_steps":["Finalize ML application bug fixes","Begin systematic collection of historical time prediction data","Consult with TMS administrator about data extraction alternatives","Document data requirements for accurate terminal time predictions","Schedule progress review with Customer Service team"]}','2025-08-19 08:31:43','2025-08-19 08:31:43');
INSERT INTO notes VALUES('0d12f3f7-ed1f-4015-8f46-5443b2242aa7','cf040508-efa2-441c-936a-33fb21cf3c5d','I need to consider data cleaning to focus more or solely on vessels and barges, as that is the only use for the jetty optimization app',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Project Status Update\u000aCurrently evaluating data quality requirements for the machine learning model that predicts ship terminal processing times at our Terneuzen facility. A key consideration has emerged regarding data cleaning and scope refinement.\u000a\u000a## Current Focus\u000aThe data cleaning process needs to be refined to focus exclusively on vessels and barges, as these are the only relevant vessel types for the jetty optimization application. This targeted approach will improve prediction accuracy and ensure the model delivers value for its intended use case.\u000a\u000a## Integration Point\u000aThe cleaned and filtered data will directly feed into the jetty optimization application, making data quality and proper vessel type filtering essential for project success.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Project Status Update\nCurrently evaluating data quality requirements for the machine learning model that predicts ship terminal processing times at our Terneuzen facility. A key consideration has emerged regarding data cleaning and scope refinement.\n\n## Current Focus\nThe data cleaning process needs to be refined to focus exclusively on vessels and barges, as these are the only relevant vessel types for the jetty optimization application. This targeted approach will improve prediction accuracy and ensure the model delivers value for its intended use case.\n\n## Integration Point\nThe cleaned and filtered data will directly feed into the jetty optimization application, making data quality and proper vessel type filtering essential for project success.","key_insights":["Data cleaning should be refined to focus exclusively on vessels and barges","Current data includes vessel types not relevant to the jetty optimization application","Targeted data filtering will improve prediction accuracy and model relevance","The jetty optimization app is the primary use case for this ML model"],"action_items":["Define specific data filtering criteria to isolate vessel and barge data","Modify data cleaning pipeline to implement the refined filtering","Validate filtered dataset to ensure all relevant vessels/barges are retained","Update documentation to reflect the narrowed data scope","Review impact of filtered dataset on ML model performance"],"risks":["Overly restrictive filtering might remove valuable data points","Insufficient data volume after filtering could reduce model accuracy","Changing data parameters might require model retraining","Stakeholders might have different definitions of vessel/barge categories"],"opportunities":["Improved model accuracy through more focused data","Reduced processing time by eliminating irrelevant data","Better alignment between ML predictions and jetty optimization needs","Potential for more specialized prediction features for specific vessel types"],"stakeholders":["Customer Service Team","Data Science/ML Team","Jetty Optimization App Users","Terneuzen Terminal Operations","Dashboard Monitoring Team"],"next_steps":["Consult with terminal operations to confirm vessel/barge classification criteria","Document the refined data scope and get stakeholder approval","Implement and test the new data filtering approach","Evaluate ML model performance with the filtered dataset","Update monitoring dashboards to reflect the refined data focus"]}','2025-08-19 08:40:08','2025-08-19 08:40:08');
INSERT INTO notes VALUES('602e58d1-e939-49ef-9ff7-7cfc1dd0b19d','cf040508-efa2-441c-936a-33fb21cf3c5d','I uncovered some quirks in the feature management of the model trainer which need fixing. Best model so far had an R squared value of 0.82. Now I only get around 0.43. I''ll keep tweaking, should be ready by end of the day. In the mean time I''ll get familiar with OpenTAS.','During the ML Terminal Time Prediction project for Terneuzen, I identified several inconsistencies in the feature management component of the model trainer that require resolution. The previous best model achieved an R-squared value of 0.82, but current iterations are only reaching approximately 0.43. I will continue optimization efforts with the goal of completing this by end of day. Simultaneously, I''ll familiarize myself with the OpenTAS system to ensure proper integration with our prediction model.','{"enhanced_content":"During the ML Terminal Time Prediction project for Terneuzen, I identified several inconsistencies in the feature management component of the model trainer that require resolution. The previous best model achieved an R-squared value of 0.82, but current iterations are only reaching approximately 0.43. I will continue optimization efforts with the goal of completing this by end of day. Simultaneously, I''ll familiarize myself with the OpenTAS system to ensure proper integration with our prediction model.","key_insights":["Previous model achieved R-squared value of 0.82","Current model performance has dropped to R-squared of 0.43","Feature management issues identified in model trainer","OpenTAS knowledge is needed for project progression"],"action_items":["Fix inconsistencies in feature management component","Continue model optimization to improve R-squared value","Learn OpenTAS system functionality","Document model performance changes and potential causes"],"risks":["Significant performance drop (from 0.82 to 0.43) may indicate underlying data or methodology issues","Timeline pressure for same-day completion may impact quality","Lack of OpenTAS familiarity could delay integration"],"opportunities":["Improving feature management could significantly enhance model performance","OpenTAS integration knowledge will benefit future projects","Documenting the troubleshooting process can inform future ML implementations"],"stakeholders":["Customer Service team","Dashboard users","ML development team","Terneuzen terminal operations"],"next_steps":["Complete feature management fixes and verify impact on model performance","Establish performance baseline in OpenTAS environment","Schedule review with Customer Service team on updated model performance","Document lessons learned from performance troubleshooting"]}','2025-08-19 09:46:14','2025-08-19 09:46:14');
INSERT INTO notes VALUES('b8e12ba3-aa8b-482f-a828-e9bde85df506','cf040508-efa2-441c-936a-33fb21cf3c5d','Still some bugs with the predictions, but initial tests give quite good results when checking some recent ships, which is promising',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Project Status Update\u000aInitial testing of the machine learning model for predicting ship terminal processing times at the Terneuzen facility has yielded promising results. When validated against recent ship data, the predictions demonstrate good accuracy despite some remaining bugs in the system. These early positive indicators suggest the model has potential for successful implementation after resolving the identified issues.\u000a\u000a## Technical Assessment\u000aThe prediction engine contains some technical bugs that require resolution before full deployment. However, the core algorithmic performance appears to be functioning as intended based on validation against recent historical ship data.\u000a\u000a## Next Phase\u000aFocus will shift to bug resolution while continuing validation testing to ensure prediction accuracy remains consistent across various operational scenarios.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Project Status Update\nInitial testing of the machine learning model for predicting ship terminal processing times at the Terneuzen facility has yielded promising results. When validated against recent ship data, the predictions demonstrate good accuracy despite some remaining bugs in the system. These early positive indicators suggest the model has potential for successful implementation after resolving the identified issues.\n\n## Technical Assessment\nThe prediction engine contains some technical bugs that require resolution before full deployment. However, the core algorithmic performance appears to be functioning as intended based on validation against recent historical ship data.\n\n## Next Phase\nFocus will shift to bug resolution while continuing validation testing to ensure prediction accuracy remains consistent across various operational scenarios.","key_insights":["Initial testing shows promising prediction accuracy when validated against recent ship data","Some technical bugs remain in the prediction system that require resolution","Core machine learning model appears to be functioning effectively despite the bugs","Early results indicate the project has good potential for successful implementation"],"action_items":["Identify and document all bugs in the prediction system","Prioritize bug fixes based on impact to prediction accuracy","Continue validation testing with expanded historical data sets","Prepare progress report for customer service team"],"risks":["Unresolved bugs could impact reliability of predictions in production environment","Potential for reduced user adoption if early implementation contains noticeable errors","Possible data quality issues affecting prediction accuracy in certain scenarios"],"opportunities":["Leverage promising initial results to build stakeholder confidence in the project","Use feedback from initial testing to further refine the model","Develop implementation plan for customer service team based on successful early results","Create monitoring dashboard to track prediction accuracy over time"],"stakeholders":["Customer Service Team","IT Development Team","Operations Management at Terneuzen facility","Data Science/ML Team"],"next_steps":["Complete comprehensive bug identification and documentation","Develop bug resolution timeline with development team","Expand validation testing to include more diverse operational scenarios","Schedule update meeting with customer service team to share initial promising results","Begin designing monitoring dashboard requirements"]}','2025-08-19 10:15:54','2025-08-19 10:15:54');
INSERT INTO notes VALUES('8d7d817c-69ce-40d6-918a-0670c6995370','cf040508-efa2-441c-936a-33fb21cf3c5d','Getting some real useful feedback from CS at Terneuzen, with regards to business rules like contract management, or VRU',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Project Status Update\u000aSignificant progress has been made in testing the machine learning model for predicting ship terminal processing times at our Terneuzen facility. Customer Service (CS) team at Terneuzen has provided valuable feedback regarding critical business rules that need to be incorporated into the model.\u000a\u000a## Key Feedback Areas\u000aThe feedback specifically highlights the importance of integrating contract management parameters and Vapor Recovery Unit (VRU) considerations into the prediction model. These factors appear to have substantial impact on terminal processing times and will need to be properly weighted in our algorithm.\u000a\u000a## Next Phase\u000aWe will need to refine the model based on this feedback and continue development of the monitoring dashboards to track prediction accuracy and business impact.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Project Status Update\nSignificant progress has been made in testing the machine learning model for predicting ship terminal processing times at our Terneuzen facility. Customer Service (CS) team at Terneuzen has provided valuable feedback regarding critical business rules that need to be incorporated into the model.\n\n## Key Feedback Areas\nThe feedback specifically highlights the importance of integrating contract management parameters and Vapor Recovery Unit (VRU) considerations into the prediction model. These factors appear to have substantial impact on terminal processing times and will need to be properly weighted in our algorithm.\n\n## Next Phase\nWe will need to refine the model based on this feedback and continue development of the monitoring dashboards to track prediction accuracy and business impact.","key_insights":["Customer Service team feedback is proving valuable for model refinement","Contract management rules significantly impact terminal processing times","VRU (Vapor Recovery Unit) operations affect prediction accuracy","Business rules need to be incorporated into the ML model","Monitoring dashboards will be essential for tracking prediction performance"],"action_items":["Analyze and document specific contract management parameters affecting terminal times","Research VRU operational patterns and integrate into the prediction model","Schedule follow-up session with CS team to validate understanding of business rules","Update ML model parameters to incorporate newly identified variables","Design dashboard KPIs that reflect both technical accuracy and business relevance"],"risks":["Model may become overly complex if too many business rules are incorporated","Customer Service team expectations may exceed technical capabilities of the ML model","Insufficient data regarding contract management specifics could impact prediction accuracy","Changes in VRU operations could require frequent model retraining"],"opportunities":["Improved prediction accuracy will enhance resource planning and customer satisfaction","Integration of business rules creates a more holistic and valuable prediction tool","Collaboration with CS team builds cross-departmental support for the initiative","Successful implementation could serve as a template for other Evos terminals"],"stakeholders":["Customer Service team at Terneuzen","Operations management","IT/Data Science team","Contract management department","VRU technical specialists"],"next_steps":["Document detailed requirements from CS feedback session","Prioritize business rules for inclusion in next model iteration","Schedule technical review of current model performance metrics","Update project timeline to reflect new development requirements","Plan validation approach for measuring improvement after business rule integration"]}','2025-08-19 10:46:30','2025-08-19 10:46:30');
INSERT INTO notes VALUES('f6f4104a-6853-458f-ad0f-1bd472ee0f6e','cf040508-efa2-441c-936a-33fb21cf3c5d','I''ll be introducing the idea to the shiftleaders tomorrow at 1pm. I''ll focus on the integration of the machine learning predictions, and the jetty planning optimizer. I can expect to get questions on business rules regarding terminal infrastructure.',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Upcoming Presentation to Shift Leaders\u000aA presentation is scheduled for tomorrow at 13:00 to introduce the machine learning terminal time prediction model and jetty planning optimizer to the shift leaders at the Terneuzen facility. The presentation will focus on:\u000a- Integration methodology of ML predictions with existing systems\u000a- Implementation of the jetty planning optimization tool\u000a- Anticipated impact on operational workflows\u000a\u000aPreparation should include developing clear responses regarding terminal infrastructure business rules, as questions on this topic are expected from the audience.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Upcoming Presentation to Shift Leaders\nA presentation is scheduled for tomorrow at 13:00 to introduce the machine learning terminal time prediction model and jetty planning optimizer to the shift leaders at the Terneuzen facility. The presentation will focus on:\n- Integration methodology of ML predictions with existing systems\n- Implementation of the jetty planning optimization tool\n- Anticipated impact on operational workflows\n\nPreparation should include developing clear responses regarding terminal infrastructure business rules, as questions on this topic are expected from the audience.","key_insights":["Shift leaders are key stakeholders for implementation success","Integration of ML predictions with jetty planning optimization is a central focus","Terminal infrastructure business rules may be a point of concern for operational staff","This presentation represents a critical communication milestone in the project"],"action_items":["Prepare presentation materials for tomorrow''s 13:00 meeting","Develop clear explanations of ML prediction integration methodology","Create visual examples of the jetty planning optimizer in action","Compile documentation on terminal infrastructure business rules","Prepare Q&A responses for anticipated operational questions"],"risks":["Potential resistance from shift leaders if terminal infrastructure rules aren''t clearly addressed","Possible misalignment between ML predictions and operational realities","Unclear integration pathway between prediction models and planning optimizer","Insufficient understanding of business rules could undermine implementation"],"opportunities":["Gain early buy-in from operational leadership","Identify practical implementation challenges before full deployment","Incorporate shift leader feedback into model refinement","Clarify integration points between ML predictions and planning optimization","Establish clear communication channels with operational teams"],"stakeholders":["Shift Leaders at Terneuzen facility","Customer Service team","Data/ML development team","Terminal Operations management","Jetty planning personnel"],"next_steps":["Finalize presentation materials with clear visuals and examples","Review terminal infrastructure business rules documentation","Prepare concise explanation of ML model functionality and benefits","Develop implementation timeline to share with shift leaders","Establish feedback mechanism for post-presentation input"]}','2025-08-19 13:19:29','2025-08-19 13:19:29');
INSERT INTO notes VALUES('ea312e31-ad76-4016-a579-56f5e996c5ba','cf040508-efa2-441c-936a-33fb21cf3c5d','Moving forward more quickly now, I''ve integrated the prediction function into the jetty planner, it can read out the models produced by the trainer. I''m removing the mock data from the app to move towards production.','The ML Terminal Time Prediction project for Terneuzen is progressing well. I have successfully integrated the prediction function into the jetty planner application, enabling it to read models generated by the trainer module. To prepare for production deployment, I am now removing all mock data from the application. This integration represents a significant step toward implementing the machine learning model that will predict ship terminal processing times at our Terneuzen facility.','{"enhanced_content":"The ML Terminal Time Prediction project for Terneuzen is progressing well. I have successfully integrated the prediction function into the jetty planner application, enabling it to read models generated by the trainer module. To prepare for production deployment, I am now removing all mock data from the application. This integration represents a significant step toward implementing the machine learning model that will predict ship terminal processing times at our Terneuzen facility.","key_insights":["Prediction function successfully integrated into jetty planner","Application can now read models from the trainer module","Mock data being removed to prepare for production environment","Project moving forward at an accelerated pace"],"action_items":["Complete removal of all mock data from the application","Test the integrated prediction function with real data","Document the integration process for knowledge transfer","Prepare for production deployment validation"],"risks":["Potential performance issues when transitioning from mock to real data","Accuracy of predictions may differ in production environment","Dependency on trainer module output quality","Possible resistance from end-users during implementation"],"opportunities":["Improve terminal resource allocation through accurate time predictions","Enhance customer service team''s planning capabilities","Create a reusable framework for future ML implementations at other facilities","Develop KPIs to measure prediction accuracy and business impact"],"stakeholders":["Customer Service team","IT department","Terneuzen facility operations team","Data science/ML specialists","Jetty planning personnel"],"next_steps":["Conduct end-to-end testing with real data","Schedule demonstration for Customer Service team","Develop monitoring dashboard to track prediction accuracy","Create user documentation and training materials","Plan phased rollout approach for production implementation"]}','2025-08-20 06:45:21','2025-08-20 06:45:21');
INSERT INTO notes VALUES('9c306bfc-1e80-4cc1-a9a1-e1dc63e89717','cf040508-efa2-441c-936a-33fb21cf3c5d','I found an API provider for vessel data that is free and should be fine to use for our app, called AISstream.io, and uses websockets',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## API Provider Identification\u000aIdentified AISstream.io as a potential data source for the vessel prediction model. This provider offers:\u000a- Free access to vessel data\u000a- Websocket-based communication protocol\u000a- Compatibility with our application requirements\u000a\u000aThis data source will support our machine learning model for predicting ship terminal processing times at the Terneuzen facility, enabling more accurate forecasting capabilities for the customer service team''s monitoring dashboards.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## API Provider Identification\nIdentified AISstream.io as a potential data source for the vessel prediction model. This provider offers:\n- Free access to vessel data\n- Websocket-based communication protocol\n- Compatibility with our application requirements\n\nThis data source will support our machine learning model for predicting ship terminal processing times at the Terneuzen facility, enabling more accurate forecasting capabilities for the customer service team''s monitoring dashboards.","key_insights":["AISstream.io identified as free vessel data API provider","Provider uses websocket protocol for data transmission","The API appears compatible with existing application requirements","This data source can support the ML terminal time prediction model"],"action_items":["Validate AISstream.io data quality and reliability","Test websocket integration with our existing systems","Document API specifications and integration requirements","Assess data coverage for vessels operating in Terneuzen area","Create proof-of-concept integration to demonstrate viability"],"risks":["Free tier limitations might affect data availability or quality","Potential service interruptions with a free API provider","Data format might require transformation for ML model compatibility","Websocket implementation could add complexity to the application architecture"],"opportunities":["Cost savings through utilization of free API vs. paid alternatives","Real-time vessel data could improve prediction accuracy","Websocket protocol enables efficient real-time data streaming","Could expand vessel tracking capabilities beyond initial requirements"],"stakeholders":["Customer Service Team","IT Development Team","Data Science/ML Team","Operations Management at Terneuzen facility","Project Sponsor"],"next_steps":["Perform technical evaluation of AISstream.io API capabilities","Create test integration to validate data quality and reliability","Review data privacy and terms of service for compliance","Present findings to project team for approval to proceed","Develop integration plan with timeline and resource requirements"]}','2025-08-20 06:47:38','2025-08-20 06:47:38');
INSERT INTO notes VALUES('0f86de39-3d31-4cf8-ab5c-9d84c4db4cec','cf040508-efa2-441c-936a-33fb21cf3c5d','I should create logging features, and be smart about it so that it can be used to gather data and learn from that',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000aThe project aims to implement a machine learning model for predicting ship terminal processing times at our Terneuzen facility. To enhance the model''s effectiveness and ensure continuous improvement, we need to develop comprehensive logging features. These features should be strategically designed to capture relevant data points that will allow for ongoing learning, model refinement, and performance monitoring.\u000a\u000aThe logging system should be integrated with the customer service team''s workflow and feed into monitoring dashboards for real-time visibility of prediction accuracy and system performance.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\nThe project aims to implement a machine learning model for predicting ship terminal processing times at our Terneuzen facility. To enhance the model''s effectiveness and ensure continuous improvement, we need to develop comprehensive logging features. These features should be strategically designed to capture relevant data points that will allow for ongoing learning, model refinement, and performance monitoring.\n\nThe logging system should be integrated with the customer service team''s workflow and feed into monitoring dashboards for real-time visibility of prediction accuracy and system performance.","key_insights":["Logging features are essential for data collection and continuous learning","Smart design of logging will improve model accuracy over time","Data gathered through logging will serve as training material for model improvements","Integration with monitoring dashboards is necessary for performance tracking"],"action_items":["Design and implement comprehensive logging features for the ML model","Establish data collection parameters that capture relevant terminal processing variables","Create a data storage and retrieval system for collected logs","Develop integration between logging system and monitoring dashboards","Define learning mechanisms to utilize collected data for model improvement"],"risks":["Insufficient logging could lead to missed learning opportunities","Poorly designed logging might capture irrelevant data, creating noise","Data privacy concerns if logging captures sensitive information","System performance impact if logging is too resource-intensive"],"opportunities":["Leverage collected data to continuously improve prediction accuracy","Create a feedback loop for model refinement based on actual vs. predicted times","Develop insights for operational improvements beyond prediction functionality","Establish benchmarks for terminal efficiency based on collected data"],"stakeholders":["Customer Service Team","Data Science/ML Team","Operations Management at Terneuzen facility","IT Infrastructure Team","Dashboard Users/Monitoring Team"],"next_steps":["Define specific logging requirements and data points to capture","Consult with stakeholders to ensure logging meets their information needs","Create technical specification for logging implementation","Develop prototype logging system and test with sample data","Establish review process for evaluating data quality from logs"]}','2025-08-21 09:00:18','2025-08-21 09:00:18');
INSERT INTO notes VALUES('4404db83-90d5-4c0d-b6ea-31f58059b7c1','cf040508-efa2-441c-936a-33fb21cf3c5d','So now from a higher level, I can see we can make a list of nominated vessels, and use the AIS API to get there relative location and estimated time of arrival (using tidal information and lock information as well). Then we have the ML prediction ready to predict Terminal Time. This information combined can create a "waiting" list of vessels which the Google OR tools function can use to make optimized planning. There are some undocumented business rules that the planning optimizer should follow though, which Tim should collect from the shift leaders. In the mean time, I should create a flexible planning tool, Gantt chart, where the shift leaders can move ships around after the model made predictions or optimizations, to suit the business rules. The shift leaders should document the reason for changing so I can make a list of improvements to the model.',unistr('# ML Terminal Time Prediction Project - Planning Optimization Framework\u000a\u000aBased on our higher-level analysis, we can develop an integrated vessel planning system with the following components:\u000a\u000a1. **Vessel Nomination List**: Create a comprehensive list of nominated vessels approaching the Terneuzen terminal.\u000a\u000a2. **Real-time Tracking**: Utilize the AIS API to monitor vessel locations and calculate estimated arrival times, incorporating critical factors such as:\u000a   - Tidal conditions\u000a   - Lock scheduling and availability\u000a\u000a3. **ML Prediction Integration**: Apply our machine learning model to predict Terminal Processing Time for each approaching vessel.\u000a\u000a4. **Dynamic Waiting List**: Combine location and prediction data to generate a prioritized vessel waiting list.\u000a\u000a5. **Automated Planning Optimization**: Implement Google OR tools to create optimized berth and resource allocation plans based on the waiting list.\u000a\u000a6. **Interactive Planning Interface**: Develop a flexible Gantt chart tool allowing shift leaders to manually adjust the automated planning recommendations.\u000a\u000a7. **Feedback Collection System**: Implement a mechanism for shift leaders to document reasons for manual plan adjustments, creating a continuous improvement feedback loop for the optimization model.'),'{"enhanced_content":"# ML Terminal Time Prediction Project - Planning Optimization Framework\n\nBased on our higher-level analysis, we can develop an integrated vessel planning system with the following components:\n\n1. **Vessel Nomination List**: Create a comprehensive list of nominated vessels approaching the Terneuzen terminal.\n\n2. **Real-time Tracking**: Utilize the AIS API to monitor vessel locations and calculate estimated arrival times, incorporating critical factors such as:\n   - Tidal conditions\n   - Lock scheduling and availability\n\n3. **ML Prediction Integration**: Apply our machine learning model to predict Terminal Processing Time for each approaching vessel.\n\n4. **Dynamic Waiting List**: Combine location and prediction data to generate a prioritized vessel waiting list.\n\n5. **Automated Planning Optimization**: Implement Google OR tools to create optimized berth and resource allocation plans based on the waiting list.\n\n6. **Interactive Planning Interface**: Develop a flexible Gantt chart tool allowing shift leaders to manually adjust the automated planning recommendations.\n\n7. **Feedback Collection System**: Implement a mechanism for shift leaders to document reasons for manual plan adjustments, creating a continuous improvement feedback loop for the optimization model.","key_insights":["Combining AIS location data with ML terminal time predictions creates a comprehensive vessel waiting list","Google OR tools can be leveraged for automated planning optimization","Undocumented business rules need to be collected from shift leaders","A flexible planning interface (Gantt chart) will allow manual adjustments to automated plans","Documenting reasons for manual adjustments will create a feedback loop for model improvement"],"action_items":["Develop vessel nomination list structure and data collection process","Integrate AIS API for real-time vessel location and ETA calculation","Configure ML terminal time prediction model to work with the planning system","Collect undocumented business rules from shift leaders","Create interactive Gantt chart planning interface","Implement feedback collection mechanism for plan adjustments","Design and build the planning optimization algorithm using Google OR tools"],"risks":["Undocumented business rules may significantly impact planning optimization effectiveness","Shift leaders might resist adopting the new planning system","AIS data quality or availability issues could affect prediction accuracy","Integration complexity between multiple data sources might delay implementation","Manual overrides could undermine the system''s learning capabilities if not properly documented"],"opportunities":["Creating a standardized set of business rules for vessel planning","Improving terminal efficiency through optimized resource allocation","Reducing vessel waiting times and associated costs","Building a continuously improving planning system through feedback loops","Potential application of similar approach at other Evos terminals"],"stakeholders":["Terminal shift leaders","Customer service team","Vessel operators/shipping companies","Terminal operations management","IT/development team","Data science team"],"next_steps":["Schedule interviews with shift leaders to document business rules","Finalize data integration plan for AIS API and ML prediction model","Develop prototype of the Gantt chart planning interface","Create specification for the planning optimization algorithm","Establish KPIs to measure the effectiveness of the planning system","Plan user training sessions for shift leaders"]}','2025-08-21 10:00:02','2025-08-21 10:00:02');
INSERT INTO notes VALUES('97ad07b2-f23b-4eb1-ac41-1784a5fc04b7','cf040508-efa2-441c-936a-33fb21cf3c5d','I should make a diagram for Richard around the architecture of the program',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000aThe project involves implementing a machine learning model to predict ship terminal processing times at our Terneuzen facility. We are collaborating with the customer service team and developing monitoring dashboards to track performance. A system architecture diagram needs to be created for Richard to clarify the technical implementation and data flow.\u000a\u000a## Current Focus\u000a- Creating a system architecture diagram to visualize the program structure and data flow\u000a- Working with customer service team to integrate the prediction model\u000a- Developing monitoring dashboards to track prediction accuracy and system performance'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\nThe project involves implementing a machine learning model to predict ship terminal processing times at our Terneuzen facility. We are collaborating with the customer service team and developing monitoring dashboards to track performance. A system architecture diagram needs to be created for Richard to clarify the technical implementation and data flow.\n\n## Current Focus\n- Creating a system architecture diagram to visualize the program structure and data flow\n- Working with customer service team to integrate the prediction model\n- Developing monitoring dashboards to track prediction accuracy and system performance","key_insights":["A system architecture diagram is needed to communicate technical implementation","The project combines ML prediction with operational dashboard development","Collaboration with the customer service team is essential for implementation"],"action_items":["Create system architecture diagram for Richard","Schedule review meeting with Richard to discuss the diagram","Identify key components to include in the monitoring dashboards","Define data flow process between ML model and customer service systems"],"risks":["Unclear architecture may lead to implementation challenges","Insufficient stakeholder understanding of the technical solution","Potential integration issues between ML model and existing systems"],"opportunities":["Improved visualization of system architecture will enhance stakeholder buy-in","Better terminal time predictions can improve customer service and operational planning","Monitoring dashboards can provide ongoing performance metrics for continuous improvement"],"stakeholders":["Richard (likely technical lead or manager)","Customer Service team","Terminal Operations team at Terneuzen","IT/Development team"],"next_steps":["Draft initial system architecture diagram","Identify key components and data flows to include in the diagram","Schedule meeting with Richard to review diagram and gather feedback","Align with Customer Service team on dashboard requirements and KPIs"]}','2025-08-21 10:01:33','2025-08-21 10:01:33');
INSERT INTO notes VALUES('de983e52-25ef-48bd-a1e9-e6501b87437d','cf040508-efa2-441c-936a-33fb21cf3c5d','I need to figure out the impact of using nomination values for predictions of the actual values. The model is trained on actual values for Quantities',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Current Focus\u000aInvestigating the impact of using nomination values in our prediction model versus actual values. The current machine learning model is trained on actual quantity values, but we need to understand implications of using nomination data instead.\u000a\u000a## Project Overview\u000aThis initiative aims to predict ship terminal processing times at our Terneuzen facility using machine learning. The project involves close collaboration with the Customer Service team and includes development of monitoring dashboards to track prediction accuracy and operational impact.\u000a\u000a## Technical Considerations\u000aThe discrepancy between nomination values (predicted/planned quantities) and actual values may impact model accuracy and reliability. This requires careful analysis to determine whether model retraining is necessary or if adjustments can be made to the existing framework.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Current Focus\nInvestigating the impact of using nomination values in our prediction model versus actual values. The current machine learning model is trained on actual quantity values, but we need to understand implications of using nomination data instead.\n\n## Project Overview\nThis initiative aims to predict ship terminal processing times at our Terneuzen facility using machine learning. The project involves close collaboration with the Customer Service team and includes development of monitoring dashboards to track prediction accuracy and operational impact.\n\n## Technical Considerations\nThe discrepancy between nomination values (predicted/planned quantities) and actual values may impact model accuracy and reliability. This requires careful analysis to determine whether model retraining is necessary or if adjustments can be made to the existing framework.","key_insights":["Current ML model is trained on actual quantity values, not nomination values","There may be a discrepancy between nomination and actual values affecting prediction accuracy","This project requires collaboration with Customer Service team","Monitoring dashboards are being developed as part of the solution"],"action_items":["Analyze historical differences between nomination values and actual values","Determine statistical significance of these differences on prediction outcomes","Consult with data science team about model retraining requirements","Test prediction accuracy using nomination values against known outcomes","Document findings for stakeholder review"],"risks":["Model accuracy may decrease if nomination values differ significantly from actuals","Operational decisions based on inaccurate predictions could impact terminal efficiency","Customer expectations management if prediction quality varies","Resource requirements for model retraining if substantial changes are needed"],"opportunities":["Earlier predictions possible if nomination values prove reliable enough","Potential to improve planning processes by identifying patterns in nomination vs. actual discrepancies","Enhanced dashboard could provide valuable insights to multiple departments","Methodology could be expanded to other Evos terminals if successful"],"stakeholders":["Customer Service team","Operations management at Terneuzen facility","Data Science/Analytics team","Terminal planning personnel","Customers utilizing the Terneuzen terminal"],"next_steps":["Gather 3-6 months of historical data comparing nomination vs. actual values","Schedule meeting with Customer Service to understand their requirements and use cases","Create test scenario to validate prediction accuracy using nomination values","Develop prototype dashboard section showing prediction confidence levels","Prepare interim findings report for next project steering committee"]}','2025-08-22 05:30:40','2025-08-22 05:30:40');
INSERT INTO notes VALUES('bedf9330-56b5-45cb-a269-ae27539b38ab','cf040508-efa2-441c-936a-33fb21cf3c5d','The testing was not started yet',unistr('# ML Terminal Time Prediction - Terneuzen\u000a\u000a## Status Update\u000aThe testing phase for the machine learning model to predict ship terminal processing times at the Terneuzen facility has not yet commenced.\u000a\u000a## Project Overview\u000aThis initiative aims to implement a predictive model for terminal processing times, working closely with the Customer Service team and developing monitoring dashboards to track performance.\u000a\u000a## Current Situation\u000aPre-testing phase - Preparations for model testing are still in progress.'),'{"enhanced_content":"# ML Terminal Time Prediction - Terneuzen\n\n## Status Update\nThe testing phase for the machine learning model to predict ship terminal processing times at the Terneuzen facility has not yet commenced.\n\n## Project Overview\nThis initiative aims to implement a predictive model for terminal processing times, working closely with the Customer Service team and developing monitoring dashboards to track performance.\n\n## Current Situation\nPre-testing phase - Preparations for model testing are still in progress.","key_insights":["Testing phase for the ML model has not started yet","Project involves collaboration with Customer Service team","Monitoring dashboards are planned as part of the implementation"],"action_items":["Define testing timeline and methodology","Confirm testing criteria and success metrics","Prepare testing environment and data sets","Align with Customer Service team on testing approach","Develop initial dashboard requirements"],"risks":["Potential delay in project timeline due to testing not yet started","Possible resource constraints affecting testing initiation","Data quality issues might impact model accuracy when testing begins"],"opportunities":["Early involvement of Customer Service team can improve model relevance","Developing comprehensive monitoring dashboards can provide valuable operational insights","Testing phase can incorporate recent operational data for improved prediction accuracy"],"stakeholders":["Customer Service team","Operations team at Terneuzen facility","Data science/ML development team","Terminal management","Project sponsors"],"next_steps":["Schedule kickoff meeting for testing phase","Establish testing timeline with clear milestones","Confirm resource availability for testing activities","Review data preparation status for testing","Develop testing protocol document"]}','2025-08-28 15:38:50','2025-08-28 15:38:50');
INSERT INTO notes VALUES('29df7de7-7e82-4838-a637-aa3de1430d0c','cf040508-efa2-441c-936a-33fb21cf3c5d','I''ve fixed the map visuals of the ships, also migrated the db to postgres, with SQLite fallback. Then I fixed the docker script, got nginx set up and a Cloudflare tunnel so I can run their user access management functions. Now I''m fixing the external fonts and such to make it similar to local.','Successfully completed several technical improvements to the ML Terminal Time Prediction system for Terneuzen: (1) Fixed map visualization functionality for ship tracking, (2) Migrated the database to PostgreSQL with SQLite fallback for redundancy, (3) Resolved Docker script issues to improve deployment reliability, (4) Implemented Nginx server configuration, (5) Established Cloudflare tunnel for secure user access management, and (6) Currently standardizing external fonts and UI elements to ensure consistency between local and production environments.','{"enhanced_content":"Successfully completed several technical improvements to the ML Terminal Time Prediction system for Terneuzen: (1) Fixed map visualization functionality for ship tracking, (2) Migrated the database to PostgreSQL with SQLite fallback for redundancy, (3) Resolved Docker script issues to improve deployment reliability, (4) Implemented Nginx server configuration, (5) Established Cloudflare tunnel for secure user access management, and (6) Currently standardizing external fonts and UI elements to ensure consistency between local and production environments.","key_insights":["Database migration to PostgreSQL improves scalability while maintaining SQLite fallback for redundancy","Implementation of Cloudflare tunnel enhances security for user access management","Technical infrastructure improvements (Docker, Nginx) strengthen system reliability"],"action_items":["Complete standardization of external fonts and UI elements","Verify consistency between local and production environments","Document the technical infrastructure changes for future maintenance","Test user access management functionality through Cloudflare"],"risks":["Potential compatibility issues between PostgreSQL and existing data structures","User access disruption during Cloudflare integration","Performance impact during database migration transition period"],"opportunities":["Leverage improved database for more sophisticated ML model training","Utilize Cloudflare''s analytics for monitoring system usage patterns","Standardized UI provides foundation for future dashboard enhancements","Improved infrastructure allows for easier scaling as prediction model expands"],"stakeholders":["Customer Service Team","IT Infrastructure Team","Data Science Team","Terneuzen Terminal Operations","Security/Access Management Team"],"next_steps":["Finalize UI standardization across environments","Conduct comprehensive testing of the PostgreSQL implementation","Schedule demonstration with Customer Service Team for feedback","Create monitoring protocol for the new infrastructure components","Document technical specifications and configuration details"]}','2025-09-02 10:48:22','2025-09-02 10:48:22');
INSERT INTO notes VALUES('f687f622-4a17-4acd-be67-1c4e812121cd','cf040508-efa2-441c-936a-33fb21cf3c5d','Questions were raised during the Operation Manager''s meeting, about maintenance and TMS connections, which are valid but we have plans for that','During the Operations Manager''s meeting for the ML Terminal Time Prediction project at Terneuzen, questions were raised regarding maintenance requirements and TMS (Terminal Management System) connections. These concerns have been acknowledged as valid, and we have already developed plans to address these integration and maintenance aspects.','{"enhanced_content":"During the Operations Manager''s meeting for the ML Terminal Time Prediction project at Terneuzen, questions were raised regarding maintenance requirements and TMS (Terminal Management System) connections. These concerns have been acknowledged as valid, and we have already developed plans to address these integration and maintenance aspects.","key_insights":["Operations management has concerns about maintenance requirements","Questions about TMS connections were raised","Both concerns are considered valid","Plans to address these issues already exist"],"action_items":["Document existing plans for TMS connections","Communicate maintenance strategy to Operations team","Verify that current plans adequately address all concerns raised"],"risks":["Insufficient maintenance planning could impact model reliability","TMS connection issues might affect data quality and prediction accuracy","Operational buy-in may be limited if concerns aren''t properly addressed"],"opportunities":["Leverage existing plans to proactively address stakeholder concerns","Use Operations feedback to strengthen implementation approach","Enhance TMS integration to improve overall model performance"],"stakeholders":["Operations Managers","Customer Service team","TMS system administrators","Maintenance team"],"next_steps":["Schedule follow-up meeting with Operations Managers to present existing plans","Create detailed documentation of TMS connection approach","Develop communication plan to ensure all stakeholders understand maintenance strategy","Review and potentially enhance current plans based on feedback received"]}','2025-09-02 10:49:49','2025-09-02 10:49:49');
INSERT INTO notes VALUES('756b0167-47cf-45cf-940b-509950d809d4','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Test note for debugging',unistr('# Continuous Improvement Platform Development\u000a\u000aThis project aims to create an integrated continuous improvement platform that guides users through all DMAIC (Define, Measure, Analyze, Improve, Control) steps and phases. The platform will serve as a comprehensive tool to standardize and facilitate improvement initiatives across Evos Amsterdam.\u000a\u000aInitial testing phase in progress to verify system functionality and user experience.\u000a\u000a*Note: Currently in debugging stage.*'),'{"enhanced_content":"# Continuous Improvement Platform Development\n\nThis project aims to create an integrated continuous improvement platform that guides users through all DMAIC (Define, Measure, Analyze, Improve, Control) steps and phases. The platform will serve as a comprehensive tool to standardize and facilitate improvement initiatives across Evos Amsterdam.\n\nInitial testing phase in progress to verify system functionality and user experience.\n\n*Note: Currently in debugging stage.*","key_insights":["Project is in early development/testing phase","Platform will incorporate complete DMAIC methodology","Goal is to create an integrated, guided user experience for continuous improvement initiatives"],"action_items":["Complete initial debugging process","Develop user journey map through DMAIC phases","Create functional requirements specification","Establish testing protocols for platform validation"],"risks":["User adoption may be challenging if platform is not intuitive","Integration with existing systems could present technical difficulties","DMAIC methodology might need adaptation for specific Evos processes"],"opportunities":["Standardize improvement processes across the organization","Create measurable tracking of improvement initiatives","Develop knowledge repository of improvement practices","Enable better collaboration between departments on improvement projects"],"stakeholders":["Operations teams","Process improvement specialists","IT department","Management team","End users/operators"],"next_steps":["Finalize debugging plan","Schedule stakeholder input sessions","Develop prototype of user interface","Create project timeline with key milestones"]}','2025-09-30 13:24:14','2025-09-30 13:24:14');
INSERT INTO notes VALUES('745a1105-65d8-4196-82b0-614568157777','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Test note for debugging with error handling',unistr('# Continuous Improvement Platform Development\u000a\u000aProject goal: Create an integral continuous improvement platform that guides users through all DMAIC (Define, Measure, Analyze, Improve, Control) steps and phases.\u000a\u000aThis is a test note for debugging with error handling functionality. Further project details and progress updates will be documented as the project advances.'),'{"enhanced_content":"# Continuous Improvement Platform Development\n\nProject goal: Create an integral continuous improvement platform that guides users through all DMAIC (Define, Measure, Analyze, Improve, Control) steps and phases.\n\nThis is a test note for debugging with error handling functionality. Further project details and progress updates will be documented as the project advances.","key_insights":["Initial project setup phase for CI platform development","DMAIC methodology will be the foundation of the platform","Error handling capabilities will be a critical component"],"action_items":["Develop test cases for error handling functionality","Document DMAIC process flow for platform implementation","Set up debugging environment"],"risks":["Inadequate error handling could impact user experience","Complexity of DMAIC implementation might extend timeline"],"opportunities":["Creating a standardized approach to continuous improvement across Evos Amsterdam","Potential for improved project success rates through guided DMAIC methodology","Error handling development provides foundation for robust platform reliability"],"stakeholders":["Development team","Continuous Improvement department","End users (Evos Amsterdam staff)"],"next_steps":["Complete initial error handling testing","Define detailed requirements for each DMAIC phase in the platform","Schedule review of debugging results with development team"]}','2025-09-30 13:25:36','2025-09-30 13:25:36');
INSERT INTO notes VALUES('dc986740-ce63-46e2-9287-732d0c8557f7','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Test note for debugging with improved error handling',unistr('# Continuous Improvement Platform Development Project\u000a\u000aThis project aims to create an integrated continuous improvement platform that guides users through all DMAIC (Define, Measure, Analyze, Improve, Control) steps and phases. The platform will standardize the improvement process and provide structured guidance for users across the organization.\u000a\u000aCurrent Status: Initial testing phase with focus on error handling improvements.\u000a\u000a## Technical Development\u000a- Error handling mechanisms being tested and refined\u000a- Debugging procedures established to ensure platform stability\u000a\u000a## Project Objectives\u000a- Create an end-to-end platform covering all DMAIC methodology steps\u000a- Ensure user-friendly guidance throughout the improvement process\u000a- Standardize continuous improvement approaches across Evos Amsterdam\u000a- Implement robust error handling for system reliability'),'{"enhanced_content":"# Continuous Improvement Platform Development Project\n\nThis project aims to create an integrated continuous improvement platform that guides users through all DMAIC (Define, Measure, Analyze, Improve, Control) steps and phases. The platform will standardize the improvement process and provide structured guidance for users across the organization.\n\nCurrent Status: Initial testing phase with focus on error handling improvements.\n\n## Technical Development\n- Error handling mechanisms being tested and refined\n- Debugging procedures established to ensure platform stability\n\n## Project Objectives\n- Create an end-to-end platform covering all DMAIC methodology steps\n- Ensure user-friendly guidance throughout the improvement process\n- Standardize continuous improvement approaches across Evos Amsterdam\n- Implement robust error handling for system reliability","key_insights":["Project focuses on creating an integrated DMAIC-based improvement platform","Currently in testing phase with emphasis on error handling","Platform aims to standardize improvement processes across the organization","Debugging procedures are being established for platform stability"],"action_items":["Complete testing of error handling mechanisms","Document debugging procedures for development team reference","Define user acceptance testing criteria for the platform","Develop user guidance content for each DMAIC phase"],"risks":["Insufficient error handling could lead to poor user experience","Complex DMAIC methodology might create steep learning curve for users","Integration with existing systems may present technical challenges","Adoption resistance if platform is not intuitive enough"],"opportunities":["Standardization of improvement processes across the organization","Enhanced data collection through structured DMAIC approach","Improved tracking of continuous improvement initiatives","Knowledge sharing and best practice distribution through the platform"],"stakeholders":["IT Development Team","Continuous Improvement Department","Operations Teams (end users)","Management (for resource allocation and strategic alignment)","Process Owners (for input on DMAIC requirements)"],"next_steps":["Finalize error handling testing and implement improvements","Develop user documentation for the platform","Schedule stakeholder review of current progress","Plan user acceptance testing with representative user groups","Create implementation and rollout strategy"]}','2025-09-30 14:04:41','2025-09-30 14:04:41');
INSERT INTO notes VALUES('77731e4a-62ef-4816-8239-b044c5864460','f5a28680-50e5-467c-9dd8-6b7f69e7202c','I''ve put together templates for both. I need to still figure out the time period, and what tools to use to automate the generation. It would be wise to have the curate step be a human in the loop step.',NULL,NULL,'2025-10-02 07:43:22','2025-10-02 07:43:22');
CREATE TABLE todos (
        id TEXT PRIMARY KEY,
        project_id TEXT REFERENCES projects(id) ON DELETE CASCADE,
        title TEXT NOT NULL,
        description TEXT,
        status TEXT DEFAULT 'pending',
        priority TEXT DEFAULT 'medium',
        due_date TEXT,
        llm_generated INTEGER DEFAULT 0,
        created_at TEXT DEFAULT (datetime('now')),
        updated_at TEXT DEFAULT (datetime('now'))
      );
INSERT INTO todos VALUES('8a070eec-2b87-40ae-9b72-31b316e2228f','cf040508-efa2-441c-936a-33fb21cf3c5d','Create OpenTAS TMS export procedure document','Document step-by-step process for exporting data from OpenTAS TMS in Terneuzen, including necessary fields, filters, and frequency for model training and validation.','pending','high',NULL,1,'2025-08-18 08:29:41','2025-08-18 08:29:41');
INSERT INTO todos VALUES('206cfb37-f296-4413-b585-ba38d7f4f30a','cf040508-efa2-441c-936a-33fb21cf3c5d','Verify existing vessel finder API accounts','Check with IT and operations teams if Evos already has any existing vessel finder API accounts that can be utilized instead of creating a new one through sales.','pending','medium',NULL,1,'2025-08-18 08:29:41','2025-08-18 08:29:41');
INSERT INTO todos VALUES('8450f95a-0a54-4233-8c7a-8754b45ed3a1','cf040508-efa2-441c-936a-33fb21cf3c5d','Develop model performance feedback loop','Design and implement a systematic process to compare ML predictions with actual terminal processing times from OpenTAS TMS to measure model accuracy and identify improvement areas.','pending','high',NULL,1,'2025-08-18 08:29:41','2025-08-18 08:29:41');
INSERT INTO todos VALUES('56d2f1f9-7d75-4202-ac12-812eefbbbd5a','cf040508-efa2-441c-936a-33fb21cf3c5d','Create executable distribution plan','Develop a plan for distributing and implementing the model executable, including user training, installation requirements, and access controls.','pending','medium',NULL,1,'2025-08-18 08:29:41','2025-08-18 08:29:41');
INSERT INTO todos VALUES('cf31322e-6ff5-4720-b79f-b9eb10510ebf','cf040508-efa2-441c-936a-33fb21cf3c5d','Design monitoring dashboard requirements','Work with customer service team to define key metrics and visualization requirements for the terminal time prediction monitoring dashboard.','pending','high',NULL,1,'2025-08-18 08:29:41','2025-08-18 08:29:41');
INSERT INTO todos VALUES('e61183aa-f56b-485b-9483-0a65ded886d6','cf040508-efa2-441c-936a-33fb21cf3c5d','Fix prediction bugs','Identify and resolve the bugs in the ML prediction functionality that were noted during recent testing. Ensure the model is producing reliable predictions consistently.','pending','high',NULL,1,'2025-08-20 06:55:03','2025-09-20 16:54:25');
INSERT INTO todos VALUES('df0dfcd7-5e38-46ec-a8a7-acf6daed9672','cf040508-efa2-441c-936a-33fb21cf3c5d','Document business rules from CS feedback','Compile and document the business rules gathered from Customer Service at Terneuzen, particularly regarding contract management and VRU (Vapor Recovery Unit) requirements.','pending','high',NULL,1,'2025-08-20 06:55:03','2025-08-20 06:55:03');
INSERT INTO todos VALUES('83b523a5-182d-4f02-8ebc-582e3da31f9c','cf040508-efa2-441c-936a-33fb21cf3c5d','Prepare follow-up with shift leaders','Based on the initial meeting with shift leaders, prepare responses to questions about terminal infrastructure and document any additional business rules that need to be incorporated into the model.','pending','medium',NULL,1,'2025-08-20 06:55:03','2025-08-20 06:55:03');
INSERT INTO todos VALUES('b7227262-a73a-413c-b0a0-48ee5ae6f8df','cf040508-efa2-441c-936a-33fb21cf3c5d','Complete AISstream.io integration','Finalize the integration of the AISstream.io API for vessel data, ensuring the websocket connection is stable and data is flowing correctly into the application.','completed','high',NULL,1,'2025-08-20 06:55:03','2025-09-20 16:54:31');
INSERT INTO todos VALUES('3ba5f7f6-2f79-409c-b886-bdd435ad7239','cf040508-efa2-441c-936a-33fb21cf3c5d','Create validation metrics dashboard','Develop a dashboard to monitor the accuracy of ML predictions compared to actual terminal processing times, allowing for continuous validation and improvement of the model.','pending','medium',NULL,1,'2025-08-20 06:55:03','2025-08-20 06:55:03');
INSERT INTO todos VALUES('b0eee519-bd78-4ebe-8d61-fae3b605b27b','cf040508-efa2-441c-936a-33fb21cf3c5d','Prepare for production deployment','Create a deployment plan for moving from development to production, including data migration strategy, testing procedures, and rollback protocols.','pending','medium',NULL,1,'2025-08-20 06:55:03','2025-08-20 06:55:03');
INSERT INTO todos VALUES('c6bd9022-f0e1-49a5-b2dd-434111292443','cf040508-efa2-441c-936a-33fb21cf3c5d','Document API integration','Create comprehensive documentation for the AISstream.io API integration, including authentication, data formats, and error handling procedures.','pending','low',NULL,1,'2025-08-20 06:55:03','2025-08-20 06:55:03');
INSERT INTO todos VALUES('333fa6e7-ea84-4f6a-bd04-f568ecfd47ee','cf040508-efa2-441c-936a-33fb21cf3c5d','Conduct user acceptance testing','Organize UAT sessions with CS team members to verify the jetty planner with integrated ML predictions meets their operational needs.','pending','medium',NULL,1,'2025-08-20 06:55:03','2025-08-20 06:55:03');
INSERT INTO todos VALUES('8964b835-71b3-4cf9-9282-e77696f66b58','cf040508-efa2-441c-936a-33fb21cf3c5d','Create system architecture diagram for Richard','Develop a comprehensive diagram showing the components and data flow of the ML terminal time prediction system, including AIS API integration, ML prediction model, and planning optimization with Google OR tools.','completed','high',NULL,1,'2025-08-21 10:03:31','2025-08-27 13:26:33');
INSERT INTO todos VALUES('5bef3508-987f-4a99-80c3-3f9fe46c1eac','cf040508-efa2-441c-936a-33fb21cf3c5d','Document business rules from shift leaders','Schedule meetings with shift leaders to collect and document the undocumented business rules that should be incorporated into the planning optimization algorithm.','pending','high',NULL,1,'2025-08-21 10:03:31','2025-08-21 10:03:31');
INSERT INTO todos VALUES('802b9675-1924-46a4-b9d1-3ac4942773dc','cf040508-efa2-441c-936a-33fb21cf3c5d','Develop flexible Gantt chart planning tool','Create an interactive Gantt chart interface that allows shift leaders to manually adjust the ML-optimized vessel schedule and document their reasoning for changes.','completed','high',NULL,1,'2025-08-21 10:03:31','2025-09-20 16:54:08');
INSERT INTO todos VALUES('bd9a14a1-2cd9-4a95-8be0-f4798f6a309e','cf040508-efa2-441c-936a-33fb21cf3c5d','Implement logging system for model improvement','Design and implement a comprehensive logging system that captures shift leader adjustments and reasons, to generate data for future model improvements and business rule automation.','completed','medium',NULL,1,'2025-08-21 10:03:31','2025-09-20 16:54:12');
INSERT INTO todos VALUES('9f7421bc-95b2-4e9d-8a0f-2e63e6210d54','cf040508-efa2-441c-936a-33fb21cf3c5d','Complete AISstream.io API integration','Finalize the integration of the AISstream.io websocket API to provide real-time vessel location data and estimated arrival times for the prediction system.','completed','medium',NULL,1,'2025-08-21 10:03:31','2025-09-20 16:54:16');
INSERT INTO todos VALUES('fff85e8c-5e7a-4435-b341-843b0deeb46c','cf040508-efa2-441c-936a-33fb21cf3c5d','Remove mock data and prepare for production','Complete the transition from development to production by removing all mock data, ensuring the system uses only real data sources and is ready for deployment.','completed','medium',NULL,1,'2025-08-21 10:03:31','2025-08-21 10:03:46');
INSERT INTO todos VALUES('441135d5-a771-43ca-9d49-1aa2628a7b44','cf040508-efa2-441c-936a-33fb21cf3c5d','Create monitoring dashboard for predictions','Develop a dashboard to monitor the accuracy of ML predictions compared to actual terminal processing times, highlighting discrepancies and potential areas for model improvement.','pending','medium',NULL,1,'2025-08-21 10:03:31','2025-08-21 10:03:31');
INSERT INTO todos VALUES('9d40254f-97b0-4be0-9132-4b259a6ee6ed','cf040508-efa2-441c-936a-33fb21cf3c5d','Plan customer service team training','Develop training materials and schedule sessions with the customer service team to ensure they understand how to use and interpret the new prediction system.','pending','low',NULL,1,'2025-08-21 10:03:31','2025-08-21 10:03:31');
INSERT INTO todos VALUES('3140e317-f05d-462f-af5c-5c15df8291f5','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Define platform requirements and scope','Create a comprehensive document outlining the key requirements, features, and scope of the DMAIC-based continuous improvement platform. Include user journeys, primary functionalities, and integration points.','pending','high',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('33824dfd-0959-4bbf-a93c-450cf187d72b','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Conduct stakeholder interviews','Schedule and conduct interviews with key stakeholders across departments to gather insights on needs, pain points, and expectations for the continuous improvement platform.','pending','high',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('320e44a1-adb2-4b24-9e23-93411e8bacd0','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Research existing DMAIC platforms and tools','Perform market research on existing continuous improvement platforms that incorporate DMAIC methodology. Identify best practices, common features, and potential integration opportunities.','pending','medium',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('06552f40-a9a7-4f30-ba13-3042c39cd57d','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Create platform architecture design','Develop a high-level architectural design for the platform, including user interface flows, database structure, and system components that will guide users through each DMAIC phase.','pending','high',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('098e07fb-65e4-4d79-9a4f-f0ca5e72c1fd','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Develop project implementation plan','Create a detailed implementation plan with timeline, resource requirements, milestones, and budget considerations for developing and launching the continuous improvement platform.','pending','medium',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('c9fe8424-a179-4f29-922d-629dba855da9','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Design DMAIC phase templates and workflows','Design standardized templates, forms, and workflows for each DMAIC phase (Define, Measure, Analyze, Improve, Control) that will guide users through the improvement process.','pending','medium',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('f70fc7e6-f009-41e6-9e0b-44946c146b93','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Identify technology stack and development approach','Evaluate and select appropriate technologies, development frameworks, and implementation approaches for building the platform. Consider build vs. buy options and integration capabilities.','pending','medium',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('ac97827c-ae32-442f-9c90-bea78cfe83ab','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Create platform prototype or mockups','Develop visual mockups or a functional prototype of the platform interface to demonstrate key features and gather early feedback from potential users.','pending','low',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
INSERT INTO todos VALUES('d2d40d82-477a-42af-b8e7-fb5c1894a4e1','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Establish success metrics and KPIs','Define clear metrics and KPIs to measure the success and effectiveness of the continuous improvement platform once implemented.','pending','low',NULL,1,'2025-08-31 17:41:24','2025-08-31 17:41:24');
CREATE TABLE reports (
        id TEXT PRIMARY KEY,
        project_id TEXT REFERENCES projects(id) ON DELETE CASCADE,
        title TEXT NOT NULL,
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        report_type TEXT NOT NULL,
        recipient TEXT,
        created_at TEXT DEFAULT (datetime('now'))
      );
INSERT INTO reports VALUES('weekly-1755500401071',NULL,'Automated Weekly Report - Success','Weekly report sent successfully','weekly_automated','t.c.verhoogt@gmail.com','2025-08-18 07:00:01');
INSERT INTO reports VALUES('d521c1a5-d11f-4a31-87ae-c492f7ab6ea1','cf040508-efa2-441c-936a-33fb21cf3c5d','Status Report - ML Terminal Time Prediction - Terneuzen',unistr('# Status Report: ML Terminal Time Prediction - Terneuzen\u000a**For: Jon**  \u000a**Date: [Current Date]**  \u000a**Prepared by: Tim Verhoogt, Continuous Improvement Advisor**\u000a\u000a## Executive Summary\u000aThe Machine Learning Terminal Time Prediction project for Terneuzen is progressing according to plan, with key technical components now ready for implementation. We''ve successfully extracted the ML model executable and prepared the prediction component for deployment. Project kickoff is scheduled for August 19, 2025, with testing to commence in parallel.\u000a\u000a## Key Achievements\u000a- **Technical Development**: Successfully extracted the executable from the latest ML model trainer and prediction component\u000a- **Repository Management**: Updated GitHub with the latest code and prepared distribution package for implementation teams\u000a- **Integration Research**: Completed initial investigation into Vessel Finder API integration possibilities\u000a\u000a## Current Challenges\u000a- **API Integration**: Account creation with Vessel Finder requires engaging with their sales process\u000a- **Action Required**: Need to verify if Evos already has existing accounts before proceeding with new account setup\u000a\u000a## Data Strategy\u000aWe''ve established a systematic data collection process utilizing the OpenTAS Terminal Management System (TMS) that will:\u000a1. Provide training inputs for the ML model\u000a2. Create an effective feedback loop for measuring model performance\u000a3. Support continuous model refinement through operational data\u000a\u000a## Project Timeline\u000a| Milestone | Status | Target Date |\u000a|-----------|--------|-------------|\u000a| Project Kickoff Meeting | Planned | August 19, 2025 |\u000a| Start Testing | Planned | August 19, 2025 |\u000a| Data Collection & Requirements | Planned | September 9, 2025 |\u000a\u000a## Next Steps\u000a1. Confirm project kickoff meeting details and attendees\u000a2. Verify existing Vessel Finder API accounts within Evos\u000a3. Prepare testing protocols and validation metrics\u000a4. Finalize data collection procedures from OpenTAS TMS\u000a\u000a## Expected Outcomes\u000aUpon successful implementation, this initiative will provide the customer service team with predictive insights through monitoring dashboards, enabling more accurate forecasting of ship processing times at the Terneuzen terminal.\u000a\u000aPlease let me know if you require any additional information or have questions regarding the project status.'),'status','Jon','2025-08-18 07:19:55');
INSERT INTO reports VALUES('weekly-1756105201608',NULL,'Automated Weekly Report - Success','Weekly report sent successfully','weekly_automated','t.c.verhoogt@gmail.com','2025-08-25 07:00:01');
INSERT INTO reports VALUES('weekly-1756710000967',NULL,'Automated Weekly Report - Success','Weekly report sent successfully','weekly_automated','t.c.verhoogt@gmail.com','2025-09-01 07:00:00');
INSERT INTO reports VALUES('weekly-1757314801394',NULL,'Automated Weekly Report - Success','Weekly report sent successfully','weekly_automated','t.c.verhoogt@gmail.com','2025-09-08 07:00:01');
INSERT INTO reports VALUES('weekly-1757919601737',NULL,'Automated Weekly Report - Success','Weekly report sent successfully','weekly_automated','t.c.verhoogt@gmail.com','2025-09-15 07:00:01');
INSERT INTO reports VALUES('weekly-1758524401079',NULL,'Automated Weekly Report - Success','Weekly report sent successfully','weekly_automated','t.c.verhoogt@gmail.com','2025-09-22 07:00:01');
CREATE TABLE milestones (
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INSERT INTO milestones VALUES('fc9c3b17-51d9-4c12-b81a-019ff364a99d','cf040508-efa2-441c-936a-33fb21cf3c5d','Start testing',NULL,'2025-08-19','planned','2025-08-18 07:18:54','2025-08-18 07:18:54');
INSERT INTO milestones VALUES('4ca97442-542d-4555-ad92-c711e38865bc','cf040508-efa2-441c-936a-33fb21cf3c5d','Project kickoff meeting',NULL,'2025-08-19','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('78c588cb-b0c7-425d-8c75-db2e908aec05','cf040508-efa2-441c-936a-33fb21cf3c5d','Data collection and requirements gathering',NULL,'2025-09-09','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('a5b42b43-84c3-4877-a117-c52a3b8e2eea','cf040508-efa2-441c-936a-33fb21cf3c5d','Initial ML model development',NULL,'2025-10-07','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('d21be6a7-4c6b-41be-8639-9417f3afd606','cf040508-efa2-441c-936a-33fb21cf3c5d','Dashboard prototype review',NULL,'2025-10-21','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('5bf56b16-e58f-4c80-bdf7-0aa3ff25e95b','cf040508-efa2-441c-936a-33fb21cf3c5d','Pilot testing with customer service team',NULL,'2025-11-11','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('6f755021-d984-41ff-9108-01f518c49937','cf040508-efa2-441c-936a-33fb21cf3c5d','Model refinement based on feedback',NULL,'2025-12-02','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('2b42eaed-ff19-4709-af35-d553611057af','cf040508-efa2-441c-936a-33fb21cf3c5d','Final implementation and training',NULL,'2025-12-16','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('1b340693-a8e9-4e80-be16-ba74a1cc42e9','cf040508-efa2-441c-936a-33fb21cf3c5d','Project evaluation and handover',NULL,'2026-01-13','planned','2025-08-18 07:19:31','2025-08-18 07:19:31');
INSERT INTO milestones VALUES('ae8db9b7-d2e9-4ae3-bd24-1682dba30c7d','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Project Initiation & Requirements Gathering',NULL,'2023-12-15','planned','2025-08-31 17:40:58','2025-08-31 17:40:58');
INSERT INTO milestones VALUES('9a9e8022-df77-4ce8-8057-f985fc9e0a4e','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Platform Design Approval',NULL,'2024-01-31','planned','2025-08-31 17:40:58','2025-08-31 17:40:58');
INSERT INTO milestones VALUES('1038d0dd-e983-4e20-9964-5bd0aba5502e','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Development Phase 1 Completion (Define & Measure modules)',NULL,'2024-03-15','planned','2025-08-31 17:40:58','2025-08-31 17:40:58');
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INSERT INTO milestones VALUES('9a105321-b606-4002-ae0e-fac7259af968','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Stakeholder Alignment Workshop',NULL,'2023-12-01','planned','2025-09-21 18:18:13','2025-09-21 18:18:13');
INSERT INTO milestones VALUES('10800ed2-05d0-4b00-983f-caa1927baf51','f5a28680-50e5-467c-9dd8-6b7f69e7202c','Vendor/Development Team Selection',NULL,'2024-01-15','planned','2025-09-21 18:18:13','2025-09-21 18:18:13');
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CREATE TABLE skills_assessment (
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INSERT INTO skills_assessment VALUES('6c20542c-3c80-4433-9fef-780606898373','default','Data Analysis','technical',5,8,5,NULL,'','2025-09-20 16:55:10','2025-09-20 16:55:10','2025-09-20 16:55:10');
CREATE TABLE achievements (
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CREATE TABLE mood_tracking (
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INSERT INTO mood_tracking VALUES('4183a20d-e209-440b-9a4e-887a9bf2eaec','default','2025-09-20',7,8,3,8,NULL,NULL,NULL,NULL,'2025-09-20 12:26:19',7);
INSERT INTO mood_tracking VALUES('11017220-f879-4d85-927c-c94e7bcb304d','default','2025-09-19',5,6,7,6,'tired,stressed','Long day at work with challenging meetings','work pressure,deadlines','coffee,deep breathing','2025-09-20 12:32:07',5);
INSERT INTO mood_tracking VALUES('7b549a62-0c32-42c9-984f-d4c127c577e7','default','2025-09-18',8,9,2,9,'productive,excited','Great progress on the new project, team meeting went well','positive feedback,project success','celebration,planning next steps','2025-09-20 12:32:11',8);
CREATE TABLE learning_paths (
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        path_name TEXT NOT NULL,
        path_description TEXT,
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CREATE TABLE coaching_sessions (
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        ai_response TEXT,
        coaching_style TEXT DEFAULT 'supportive',
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INSERT INTO coaching_sessions VALUES('0f0126f8-8b31-413f-ac12-4b62bd3589bc','default','mood_adaptive','Mood-Adaptive Coaching','I am feeling overwhelmed with work today',unistr('1. Acknowledgment:\u000aI hear that you''re feeling overwhelmed today, Tim. Given your generally stable mood (6.7/10) and moderate stress levels (4/10), it''s completely normal to have days where things feel more challenging. It''s good that you''re acknowledging these feelings rather than trying to push through them without recognition.\u000a\u000a2. Insights:\u000aLooking at your energy levels (7.7/10), you typically maintain good vitality for handling challenges. This suggests that while you''re feeling overwhelmed, you likely have the internal resources to manage this situation. In your continuous improvement work at Evos Amsterdam, you''re used to breaking down complex problems into manageable parts - this same approach can help here.\u000a\u000a3. Specific Suggestions:\u000aLet''s try a few strategies that align with your systematic approach:\u000a- Take 5 minutes to list everything that''s contributing to feeling overwhelmed\u000a- Identify 2-3 highest priority tasks that would give you the most relief if completed\u000a- Consider which tasks could be delegated or postponed, especially given your stakeholder relationships\u000a- Schedule short breaks between tasks to maintain your typically good energy levels\u000a- Use your project management skills to create clear boundaries around work items\u000a\u000a4. Follow-up Questions:\u000a- What specific aspects of work are feeling most overwhelming right now?\u000a- On a scale of 1-10, how urgent do these tasks feel, and how urgent are they actually?\u000a- What strategies have helped you manage similar situations in the past, particularly during your ML model testing projects?\u000a- Would it be helpful to discuss ways to better structure your workload using your continuous improvement expertise?\u000a\u000aRemember, you''ve successfully navigated challenging periods before, and your current energy levels suggest you have the resources to handle this - it''s just about finding the right approach. Would you like to explore any of these suggestions in more detail?'),'mood_adaptive',NULL,NULL,NULL,NULL,'{"coaching_insights":[],"suggested_actions":["I hear that you''re feeling overwhelmed today, Tim. Given your generally stable mood (6.7/10) and moderate stress levels (4/10), it''s completely normal to have days where things feel more challenging. It''s good that you''re acknowledging these feelings rather than trying to push through them without recognition.","Looking at your energy levels (7.7/10), you typically maintain good vitality for handling challenges. This suggests that while you''re feeling overwhelmed, you likely have the internal resources to manage this situation. In your continuous improvement work at Evos Amsterdam, you''re used to breaking down complex problems into manageable parts - this same approach can help here.","Let''s try a few strategies that align with your systematic approach:"],"follow_up_questions":["- What specific aspects of work are feeling most overwhelming right now?","- On a scale of 1-10, how urgent do these tasks feel, and how urgent are they actually?"]}',NULL,'2025-09-20T12:35:48.559Z');
INSERT INTO coaching_sessions VALUES('10bd9579-9938-4c02-9c3f-32849ac1e15e','default','mood_adaptive','Mood-Adaptive Coaching','I am feeling great today and want to tackle some big challenges',unistr('1. Acknowledgment:\u000aI''m really glad to hear you''re feeling great today, Tim! That positive energy aligns well with your recent mood trending (7/10) and particularly your strong energy levels (7.7/10). It''s excellent that you''re channeling this momentum toward taking on challenges.\u000a\u000a2. Insights/Observations:\u000aYour combination of high energy and moderate stress levels (4/10) actually creates an ideal state for tackling bigger challenges - you have the drive without feeling overwhelmed. Given your background in continuous improvement projects at Evos Amsterdam, this could be a perfect time to initiate something meaningful.\u000a\u000a3. Specific Suggestions:\u000aLet''s capitalize on this positive state while maintaining balance:\u000a- Consider identifying 2-3 high-impact challenges that align with your current projects (ML model testing or stakeholder engagement)\u000a- Break down one major challenge into smaller, manageable components using your systematic approach\u000a- Set aside specific time blocks for focused work while maintaining your valued work-life balance\u000a- Document your approach and progress - this can be valuable for future reference in your continuous improvement work\u000a\u000a4. Follow-up Questions:\u000aTo help focus this positive energy effectively:\u000a- What specific challenges are you most excited to tackle right now?\u000a- How would you like to feel at the end of the day after making progress on these challenges?\u000a- Which of your past successful approaches might you apply to these new challenges?\u000a- What support or resources would help you maintain this momentum?\u000a\u000aRemember, while it''s great to harness this positive energy, we want to ensure sustainable progress that aligns with your professional growth goals. How would you like to prioritize these challenges?'),'mood_adaptive',NULL,NULL,NULL,NULL,'{"coaching_insights":[],"suggested_actions":[],"follow_up_questions":["- What specific challenges are you most excited to tackle right now?","- How would you like to feel at the end of the day after making progress on these challenges?"]}',NULL,'2025-09-20T12:36:01.794Z');
CREATE TABLE best_practices (
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        success_metrics TEXT,
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CREATE TABLE reflection_templates (
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INSERT INTO reflection_templates VALUES('dd90bf84-b6f9-4c20-9dcf-4512f0263b21','default','Daily Check-in','daily','daily','["How am I feeling about today''s progress?","What was my biggest win today?","What challenged me the most today?","What did I learn today?","How can I improve tomorrow?"]',1,1,NULL,0,'2025-09-20 12:26:12','2025-09-20 12:26:12');
INSERT INTO reflection_templates VALUES('c16f80d2-f360-471f-9c14-bf293c1610e5','default','Weekly Reflection','weekly','weekly','["What were my key accomplishments this week?","What patterns do I notice in my work and mood?","What skills did I develop or practice this week?","What would I do differently next week?","What am I grateful for this week?","What are my priorities for next week?"]',1,1,NULL,0,'2025-09-20 12:26:12','2025-09-20 12:26:12');
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INSERT INTO reflection_templates VALUES('1dac2d60-7033-448e-8330-b6da9aeaa5ad','default','Weekly Reflection','weekly','weekly','["What were my key accomplishments this week?","What patterns do I notice in my work and mood?","What skills did I develop or practice this week?","What would I do differently next week?","What am I grateful for this week?","What are my priorities for next week?"]',1,1,NULL,0,'2025-09-20 13:04:19','2025-09-20 13:04:19');
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CREATE TABLE reflection_responses (
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CREATE TABLE work_life_metrics (
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CREATE TABLE coping_strategies (
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        user_id TEXT DEFAULT 'default',
        strategy_name TEXT NOT NULL,
        strategy_category TEXT NOT NULL,
        description TEXT NOT NULL,
        instructions TEXT NOT NULL,
        duration_minutes INTEGER,
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        stress_levels TEXT,
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      );
INSERT INTO coping_strategies VALUES('4f831bd6-642b-4b48-896a-ff39cf6b0c08','default','Deep Breathing Exercise','breathing','A simple 4-7-8 breathing technique to quickly reduce stress and anxiety','Inhale for 4 counts, hold for 7 counts, exhale for 8 counts. Repeat 4 times.',2,1,4.0,1,'2025-09-20T12:40:24.743Z',0,'anxious,stressed,overwhelmed','6,7,8,9,10','work pressure,deadlines,meetings',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('9d8ff01d-8b0d-4301-98b3-3a43d846865e','default','Progressive Muscle Relaxation','physical','Systematically tense and relax muscle groups to release physical tension','Start with your toes, tense for 5 seconds, then relax. Work your way up through each muscle group to your head.',10,2,0.0,0,NULL,0,'tense,stressed,anxious','5,6,7,8,9','physical tension,stress,anxiety',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('2e20ce5a-e7c1-4ff4-aa83-67bb2233e9ce','default','Mindful Walking','mindfulness','A short mindful walk to reset and refocus your mind','Walk slowly and deliberately, focusing on each step and your breathing. Notice your surroundings without judgment.',5,1,0.0,0,NULL,0,'restless,stressed,unfocused','4,5,6,7,8','mental fatigue,concentration issues',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('08fdbcba-95b9-426d-9799-ea429ae9f325','default','Gratitude Journaling','cognitive','Write down three things you are grateful for to shift perspective','Take 2 minutes to write down three specific things you are grateful for today. Be detailed and specific.',3,1,0.0,0,NULL,0,'negative,down,stressed','3,4,5,6,7','negative thinking,low mood',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('89e92a67-e5b3-429e-8e12-be0e08f34861','default','Quick Body Scan','mindfulness','A brief body scan to check in with physical sensations and release tension','Close your eyes and mentally scan your body from head to toe. Notice any tension and consciously release it.',3,1,0.0,0,NULL,0,'tense,stressed,unfocused','4,5,6,7,8','physical tension,stress',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('96141316-76d2-47ff-b0d4-fd5ed06bd6b0','default','Positive Affirmations','cognitive','Repeat positive statements to boost confidence and shift mindset','Choose 2-3 positive statements about your abilities and repeat them with conviction for 1-2 minutes.',2,1,0.0,0,NULL,0,'doubtful,low confidence,stressed','4,5,6,7,8','self-doubt,low confidence',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('23521373-c649-4bd5-99b3-f8d767f2574a','default','Quick Meditation','mindfulness','A brief meditation to center yourself and find calm','Sit comfortably, close your eyes, and focus on your breath. When your mind wanders, gently return to your breath.',5,2,0.0,0,NULL,0,'anxious,stressed,overwhelmed','5,6,7,8,9','anxiety,overwhelm,stress',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('4912438c-fb2b-4518-9df1-daca0abcc4c3','default','Task Prioritization','cognitive','Organize and prioritize tasks to reduce overwhelm and increase focus','List all current tasks, then categorize them by urgency and importance. Focus on the most critical 2-3 tasks.',5,2,0.0,0,NULL,0,'overwhelmed,stressed,unfocused','6,7,8,9,10','work overload,deadlines,multiple tasks',1,'2025-09-20 12:40:15','2025-09-20 12:40:15');
INSERT INTO coping_strategies VALUES('5af9af9f-9f42-4505-8816-7178066e9b58','default','Quick Stretch Break','physical','A simple 2-minute stretching routine to release tension','Stand up, reach for the sky, then touch your toes. Repeat 3 times.',2,1,4.0,1,'2025-09-20T12:44:21.390Z',1,'tense,stressed','5,6,7,8','sitting too long,work pressure',1,'2025-09-20 12:44:17','2025-09-20 12:44:17');
INSERT INTO coping_strategies VALUES('9da6e7b8-9193-42d5-9edb-7f01c1a53912','default','Deep Breathing Exercise','breathing','A simple 4-7-8 breathing technique to quickly reduce stress and anxiety','Inhale for 4 counts, hold for 7 counts, exhale for 8 counts. Repeat 4 times.',2,1,0.0,0,NULL,0,'anxious,stressed,overwhelmed','6,7,8,9,10','work pressure,deadlines,meetings',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('584d31d3-8e98-4048-bd1e-105f3ec8c949','default','Progressive Muscle Relaxation','physical','Systematically tense and relax muscle groups to release physical tension','Start with your toes, tense for 5 seconds, then relax. Work your way up through each muscle group to your head.',10,2,0.0,0,NULL,0,'tense,stressed,anxious','5,6,7,8,9','physical tension,stress,anxiety',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('87b07f28-7e52-4636-abd1-ce8d43ba72ee','default','Mindful Walking','mindfulness','A short mindful walk to reset and refocus your mind','Walk slowly and deliberately, focusing on each step and your breathing. Notice your surroundings without judgment.',5,1,0.0,0,NULL,0,'restless,stressed,unfocused','4,5,6,7,8','mental fatigue,concentration issues',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('69b524e0-a61f-4497-b0cb-2d012f0752e2','default','Gratitude Journaling','cognitive','Write down three things you are grateful for to shift perspective','Take 2 minutes to write down three specific things you are grateful for today. Be detailed and specific.',3,1,0.0,0,NULL,0,'negative,down,stressed','3,4,5,6,7','negative thinking,low mood',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('9fcb5eb2-44a9-49ec-b846-4f7d5cdd4de6','default','Quick Body Scan','mindfulness','A brief body scan to check in with physical sensations and release tension','Close your eyes and mentally scan your body from head to toe. Notice any tension and consciously release it.',3,1,0.0,0,NULL,0,'tense,stressed,unfocused','4,5,6,7,8','physical tension,stress',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('659f6e7b-bee6-46e7-8254-1a7e71209034','default','Positive Affirmations','cognitive','Repeat positive statements to boost confidence and shift mindset','Choose 2-3 positive statements about your abilities and repeat them with conviction for 1-2 minutes.',2,1,0.0,0,NULL,0,'doubtful,low confidence,stressed','4,5,6,7,8','self-doubt,low confidence',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('79b2cc41-41da-40a6-9a4f-1b70571159ca','default','Quick Meditation','mindfulness','A brief meditation to center yourself and find calm','Sit comfortably, close your eyes, and focus on your breath. When your mind wanders, gently return to your breath.',5,2,0.0,0,NULL,0,'anxious,stressed,overwhelmed','5,6,7,8,9','anxiety,overwhelm,stress',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('084f176d-5c2e-4660-a54a-98ced0febeb8','default','Task Prioritization','cognitive','Organize and prioritize tasks to reduce overwhelm and increase focus','List all current tasks, then categorize them by urgency and importance. Focus on the most critical 2-3 tasks.',5,2,0.0,0,NULL,0,'overwhelmed,stressed,unfocused','6,7,8,9,10','work overload,deadlines,multiple tasks',1,'2025-09-20 13:04:19','2025-09-20 13:04:19');
INSERT INTO coping_strategies VALUES('d610abcb-e79d-47da-932c-df996728dec1','default','Deep Breathing Exercise','breathing','A simple 4-7-8 breathing technique to quickly reduce stress and anxiety','Inhale for 4 counts, hold for 7 counts, exhale for 8 counts. Repeat 4 times.',2,1,0.0,0,NULL,0,'anxious,stressed,overwhelmed','6,7,8,9,10','work pressure,deadlines,meetings',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('f2b21056-38ae-4867-9d68-f0cf11a7559b','default','Progressive Muscle Relaxation','physical','Systematically tense and relax muscle groups to release physical tension','Start with your toes, tense for 5 seconds, then relax. Work your way up through each muscle group to your head.',10,2,0.0,0,NULL,0,'tense,stressed,anxious','5,6,7,8,9','physical tension,stress,anxiety',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('bb8c11f4-2d4a-45ae-bffb-99cfa65db1da','default','Mindful Walking','mindfulness','A short mindful walk to reset and refocus your mind','Walk slowly and deliberately, focusing on each step and your breathing. Notice your surroundings without judgment.',5,1,0.0,0,NULL,0,'restless,stressed,unfocused','4,5,6,7,8','mental fatigue,concentration issues',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('58ef9692-6c01-4739-b669-0a856426f98a','default','Gratitude Journaling','cognitive','Write down three things you are grateful for to shift perspective','Take 2 minutes to write down three specific things you are grateful for today. Be detailed and specific.',3,1,0.0,0,NULL,0,'negative,down,stressed','3,4,5,6,7','negative thinking,low mood',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('56460c03-3bf9-45f2-9968-d2ecb4a73501','default','Quick Body Scan','mindfulness','A brief body scan to check in with physical sensations and release tension','Close your eyes and mentally scan your body from head to toe. Notice any tension and consciously release it.',3,1,0.0,0,NULL,0,'tense,stressed,unfocused','4,5,6,7,8','physical tension,stress',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('650117b0-456d-4fea-89e0-0a4401bb2a23','default','Positive Affirmations','cognitive','Repeat positive statements to boost confidence and shift mindset','Choose 2-3 positive statements about your abilities and repeat them with conviction for 1-2 minutes.',2,1,0.0,0,NULL,0,'doubtful,low confidence,stressed','4,5,6,7,8','self-doubt,low confidence',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('fb1a7402-5d06-48b4-a548-204013818105','default','Quick Meditation','mindfulness','A brief meditation to center yourself and find calm','Sit comfortably, close your eyes, and focus on your breath. When your mind wanders, gently return to your breath.',5,2,0.0,0,NULL,0,'anxious,stressed,overwhelmed','5,6,7,8,9','anxiety,overwhelm,stress',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
INSERT INTO coping_strategies VALUES('3dcb69ae-93a5-44e0-99ad-6453e2ecdf22','default','Task Prioritization','cognitive','Organize and prioritize tasks to reduce overwhelm and increase focus','List all current tasks, then categorize them by urgency and importance. Focus on the most critical 2-3 tasks.',5,2,0.0,0,NULL,0,'overwhelmed,stressed,unfocused','6,7,8,9,10','work overload,deadlines,multiple tasks',1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
CREATE TABLE coping_strategy_usage (
        id TEXT PRIMARY KEY,
        user_id TEXT DEFAULT 'default',
        strategy_id TEXT NOT NULL,
        used_at TEXT NOT NULL,
        mood_before INTEGER,
        mood_after INTEGER,
        stress_before INTEGER,
        stress_after INTEGER,
        effectiveness_rating INTEGER CHECK (effectiveness_rating >= 1 AND effectiveness_rating <= 5),
        notes TEXT,
        context TEXT,
        created_at TEXT DEFAULT (datetime('now')),
        FOREIGN KEY (strategy_id) REFERENCES coping_strategies(id)
      );
INSERT INTO coping_strategy_usage VALUES('3c6d8db7-3881-4c30-ae23-dfaba39d4246','default','4f831bd6-642b-4b48-896a-ff39cf6b0c08','2025-09-20T12:40:24.743Z',6,8,7,4,4,'Really helped me calm down during a stressful meeting','work meeting','2025-09-20 12:40:24');
INSERT INTO coping_strategy_usage VALUES('8f634e29-8e15-4dd0-a60e-3e4578788374','default','5af9af9f-9f42-4505-8816-7178066e9b58','2025-09-20T12:44:21.390Z',5,7,7,4,4,'Really helped me feel better during a stressful meeting','work meeting','2025-09-20 12:44:21');
CREATE TABLE intervention_logs (
        id TEXT PRIMARY KEY,
        user_id TEXT DEFAULT 'default',
        trigger_type TEXT NOT NULL,
        action_taken TEXT,
        notes TEXT,
        effectiveness INTEGER CHECK (effectiveness >= 1 AND effectiveness <= 5),
        created_at TEXT DEFAULT (datetime('now'))
      );
CREATE TABLE workload_tracking (
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        user_id TEXT DEFAULT 'default',
        work_date TEXT NOT NULL,
        start_time TEXT NOT NULL,
        end_time TEXT NOT NULL,
        break_duration INTEGER DEFAULT 0,
        intensity_level INTEGER NOT NULL CHECK (intensity_level >= 1 AND intensity_level <= 10),
        focus_level INTEGER CHECK (focus_level >= 1 AND focus_level <= 10),
        productivity_score INTEGER CHECK (productivity_score >= 1 AND productivity_score <= 10),
        tasks_completed INTEGER DEFAULT 0,
        notes TEXT,
        work_type TEXT,
        location TEXT,
        created_at TEXT DEFAULT (datetime('now')),
        updated_at TEXT DEFAULT (datetime('now')),
        UNIQUE(user_id, work_date)
      );
CREATE TABLE work_preferences (
        id TEXT PRIMARY KEY,
        user_id TEXT DEFAULT 'default',
        max_daily_hours REAL DEFAULT 8.0,
        max_weekly_hours REAL DEFAULT 40.0,
        preferred_start_time TEXT DEFAULT '09:00',
        preferred_end_time TEXT DEFAULT '17:00',
        break_duration_minutes INTEGER DEFAULT 60,
        max_intensity_level INTEGER DEFAULT 8,
        stress_threshold INTEGER DEFAULT 7,
        weekend_work_allowed BOOLEAN DEFAULT 0,
        max_weekend_hours REAL DEFAULT 4.0,
        overtime_threshold_hours REAL DEFAULT 2.0,
        break_reminder_interval INTEGER DEFAULT 90,
        work_life_balance_goal TEXT DEFAULT 'balanced',
        notification_preferences TEXT DEFAULT '{"email": true, "browser": true, "mobile": false}',
        auto_break_suggestions BOOLEAN DEFAULT 1,
        intensity_warnings BOOLEAN DEFAULT 1,
        overwork_alerts BOOLEAN DEFAULT 1,
        weekly_summary BOOLEAN DEFAULT 1,
        created_at TEXT DEFAULT (datetime('now')),
        updated_at TEXT DEFAULT (datetime('now')),
        UNIQUE(user_id)
      );
INSERT INTO work_preferences VALUES('0c65889c-09b9-4195-936e-269a6513ed60','default',8.0,40.0,'09:00','17:00',60,8,7,0,4.0,2.0,90,'balanced','{"email": true, "browser": true, "mobile": false}',1,1,1,1,'2025-09-20 13:10:01','2025-09-20 13:10:01');
CREATE TABLE gratitude_entries (
          id TEXT PRIMARY KEY,
          user_id TEXT DEFAULT 'default',
          gratitude_date TEXT NOT NULL,
          category TEXT,
          prompt TEXT NOT NULL,
          response TEXT NOT NULL,
          mood_before INTEGER,
          mood_after INTEGER,
          achievement_id TEXT,
          tags TEXT,
          created_at TEXT DEFAULT (datetime('now')),
          updated_at TEXT DEFAULT (datetime('now')),
          FOREIGN KEY (achievement_id) REFERENCES achievements(id)
        );
CREATE INDEX idx_notes_project_id ON notes(project_id);
CREATE INDEX idx_notes_created_at ON notes(created_at DESC);
CREATE INDEX idx_todos_project_id ON todos(project_id);
CREATE INDEX idx_todos_status ON todos(status);
CREATE INDEX idx_reports_project_id ON reports(project_id);
CREATE INDEX idx_projects_status ON projects(status);
CREATE INDEX idx_milestones_project_id ON milestones(project_id);
CREATE INDEX idx_milestones_target_date ON milestones(target_date);
CREATE INDEX idx_user_preferences_category ON user_preferences(user_id, preference_category);
CREATE INDEX idx_user_preferences_key ON user_preferences(user_id, preference_key);
CREATE INDEX idx_skills_assessment_user ON skills_assessment(user_id);
CREATE INDEX idx_skills_assessment_category ON skills_assessment(user_id, skill_category);
CREATE INDEX idx_skills_last_assessed ON skills_assessment(last_assessed_at DESC);
CREATE INDEX idx_achievements_user ON achievements(user_id);
CREATE INDEX idx_achievements_status ON achievements(user_id, status);
CREATE INDEX idx_achievements_type ON achievements(user_id, achievement_type);
CREATE INDEX idx_mood_tracking_user ON mood_tracking(user_id);
CREATE INDEX idx_mood_tracking_date ON mood_tracking(user_id, mood_date DESC);
CREATE INDEX idx_learning_paths_user ON learning_paths(user_id);
CREATE INDEX idx_learning_paths_status ON learning_paths(user_id, status);
CREATE INDEX idx_coaching_sessions_user ON coaching_sessions(user_id);
CREATE INDEX idx_coaching_sessions_type ON coaching_sessions(user_id, session_type);
CREATE INDEX idx_coaching_sessions_date ON coaching_sessions(created_at DESC);
CREATE INDEX idx_best_practices_user ON best_practices(user_id);
CREATE INDEX idx_best_practices_category ON best_practices(user_id, practice_category);
CREATE INDEX idx_best_practices_tags ON best_practices(tags);
CREATE INDEX idx_reflection_templates_user ON reflection_templates(user_id);
CREATE INDEX idx_reflection_templates_active ON reflection_templates(user_id, is_active);
CREATE INDEX idx_reflection_responses_user ON reflection_responses(user_id);
CREATE INDEX idx_reflection_responses_template ON reflection_responses(template_id);
CREATE INDEX idx_reflection_responses_date ON reflection_responses(reflection_date DESC);
CREATE INDEX idx_work_life_metrics_user ON work_life_metrics(user_id);
CREATE INDEX idx_work_life_metrics_date ON work_life_metrics(user_id, metric_date DESC);
CREATE INDEX idx_coping_strategies_user ON coping_strategies(user_id);
CREATE INDEX idx_coping_strategies_category ON coping_strategies(user_id, strategy_category);
CREATE INDEX idx_coping_strategies_active ON coping_strategies(user_id, is_active);
CREATE INDEX idx_coping_strategies_effectiveness ON coping_strategies(user_id, effectiveness_rating DESC);
CREATE INDEX idx_coping_usage_user ON coping_strategy_usage(user_id);
CREATE INDEX idx_coping_usage_strategy ON coping_strategy_usage(strategy_id);
CREATE INDEX idx_coping_usage_date ON coping_strategy_usage(user_id, used_at DESC);
CREATE INDEX idx_intervention_logs_user ON intervention_logs(user_id);
CREATE INDEX idx_intervention_logs_trigger ON intervention_logs(user_id, trigger_type);
CREATE INDEX idx_intervention_logs_date ON intervention_logs(created_at DESC);
CREATE INDEX idx_workload_tracking_user ON workload_tracking(user_id);
CREATE INDEX idx_workload_tracking_date ON workload_tracking(user_id, work_date DESC);
CREATE INDEX idx_workload_tracking_type ON workload_tracking(user_id, work_type);
CREATE INDEX idx_work_preferences_user ON work_preferences(user_id);
COMMIT;