How shreyaasdeworld/seip works
This repository combines a synthetic ITSM data generator with a database-backed SEIP Phase 1 analysis and review workflow. Operators generate relational ITSM records, then run evidence extraction, correlation, activity reconstruction, capacity-leakage analysis, and reviewer-queue construction; the Streamlit dashboard presents the results. The README also describes broader causal, investment, and outcome-verification domains as proposed architecture, but the sampled source and tree do not establish implementations for them. Pipeline connections reflect explicit table reads/writes and documented ordering; unsampled implementations are not inferred.
Main entry points: generator.py ITSM Generator · seip_phase1_dashboard.py Phase 1 Dashboard · 02_build_evidence.py Evidence Builder · 03_build_correlation.py Correlation Builder
Components 10
Data Generation
- ITSM Generator
generator.py
Evidence Processing
- Evidence Builder
Database/02_build_evidence.py - Correlation Builder
Database/03_build_correlation.py
Intelligence Pipeline
- Activity Reconstruction
Database/04_reconstruct_activity.py - Capacity Leakage
Database/05_build_capacity_leakage.py
Review Interface
- Reviewer Queue Builder
Database/06_build_reviewer_queue.py - Phase 1 Dashboard
seip_phase1_dashboard.py
Other components
- Operator
- PostgreSQL
- Reviewer
Connections 9
- Operator → ITSM Generator: runs
- ITSM Generator → PostgreSQL: writes ITSM records
- Evidence Builder → PostgreSQL: reads source records; writes evidence
- Correlation Builder → PostgreSQL: reads evidence; writes correlations
- Activity Reconstruction → PostgreSQL: reads histories and correlations; writes activities
- Capacity Leakage → PostgreSQL: reads activities and correlations; writes leakage
- Reviewer Queue Builder → PostgreSQL: reads leakage; writes queue
- Phase 1 Dashboard → PostgreSQL: queries review data
- Reviewer → Phase 1 Dashboard: reviews results