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- Deleted cookbook/use_cases/trading/01_Risk_Assessment.ipynb - Deleted cookbook/use_cases/trading/02_News_Sentiment_Analysis.ipynb - Deleted cookbook/use_cases/supply_chain/02_Supply_Chain_Risk_Management.ipynb - Removed empty trading directory - Updated documentation to reflect 14 cookbooks (down from 15) - Removed all references from README.md, docs/cookbook.md, docs/use-cases.md, docs/index.md, and STRATEGIES_SUMMARY.md
106 lines
5.1 KiB
Markdown
106 lines
5.1 KiB
Markdown
# Deduplication & Conflict Resolution Strategies Summary
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## Quick Reference by Use Case
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| Use Case | Deduplication Method | Merge Strategy | Conflict Detection | Conflict Resolution |
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|----------|---------------------|----------------|-------------------|---------------------|
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| **Finance** |
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| `01_Financial_Data_Integration_MCP` | `DuplicateDetector` (incremental) | `keep_highest_confidence` | `temporal` | `most_recent` |
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| `02_Fraud_Detection` | `ClusterBuilder` (graph_based) | `merge_all` | `logical` | `expert_review` |
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| **Biomedical** |
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| `01_Drug_Discovery_Pipeline` | `EntityResolver` (semantic) | - | `relationship` | `voting` |
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| `02_Genomic_Variant_Analysis` | `DuplicateDetector` (group) | `keep_most_complete` | `value` | `credibility_weighted` |
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| **Cybersecurity** |
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| `01_Real_Time_Anomaly_Detection` | `DuplicateDetector` (pairwise) | `keep_first` | `entity` | `first_seen` |
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| `02_Threat_Intelligence_Hybrid_RAG` | `EntityResolver` (exact) | - | `type` | `highest_confidence` |
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| **Blockchain** |
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| `01_DeFi_Protocol_Intelligence` | `DuplicateDetector` (group) | `keep_last` | `relationship` | `voting` |
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| `02_Transaction_Network_Analysis` | `ClusterBuilder` (hierarchical) | `keep_most_complete` | `temporal` | `most_recent` |
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| **Intelligence** |
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| `01_Criminal_Network_Analysis` | `EntityResolver` (fuzzy) | - | `value` | `credibility_weighted` |
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| `02_Intelligence_Analysis_Orchestrator_Worker` | `DuplicateDetector` (batch) | `merge_all` | `entity` | `voting` |
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| **Renewable Energy** |
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| `01_Energy_Market_Analysis` | `DuplicateDetector` (pairwise) | `keep_highest_confidence` | `temporal` | `most_recent` |
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| **Supply Chain** |
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| `01_Supply_Chain_Data_Integration` | `DuplicateDetector` (incremental) | `keep_most_complete` | `value` | `credibility_weighted` |
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---
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## Strategy Rationale by Domain
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### Finance
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- **Financial Data Integration**: Incremental for streaming data; most_recent for time-sensitive financial data
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- **Fraud Detection**: Graph-based clustering for fraud groups; expert_review for fraud assessment
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### Biomedical
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- **Drug Discovery**: Semantic matching for drug compounds; voting for research source aggregation
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- **Genomic Variants**: Group method for related variants; credibility weighting for research sources
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### Cybersecurity
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- **Real-Time Anomaly**: Pairwise for real-time streams; keep_first for first detection priority
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- **Threat Intelligence**: Exact matching for IOCs; highest_confidence for threat classification
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### Blockchain
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- **DeFi Protocols**: Group method for related protocols; keep_last for latest protocol info
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- **Transaction Networks**: Hierarchical clustering for nested groups; temporal for time-sensitive data
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### Intelligence
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- **Criminal Networks**: Fuzzy matching for intelligence data; credibility weighting for intelligence sources
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- **Intelligence Analysis**: Batch for multi-source integration; merge_all to combine all intelligence sources
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### Renewable Energy
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- **Energy Markets**: Pairwise for real-time market data; most_recent for time-sensitive energy data
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### Supply Chain
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- **Supply Chain Integration**: Incremental for continuous updates; credibility weighting for supply chain sources
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---
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## Method Distribution
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### Deduplication Methods (9 total)
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- `pairwise`: 2 notebooks (real-time processing)
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- `batch`: 3 notebooks (large datasets)
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- `incremental`: 2 notebooks (streaming/continuous)
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- `group`: 2 notebooks (related entities)
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- `graph_based` (ClusterBuilder): 2 notebooks (interconnected entities)
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- `hierarchical` (ClusterBuilder): 1 notebook (nested groups)
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- `exact` (EntityResolver): 1 notebook (exact matching)
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- `semantic` (EntityResolver): 2 notebooks (semantic similarity)
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- `fuzzy` (EntityResolver): 1 notebook (fuzzy matching)
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### Merge Strategies (5 total)
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- `keep_first`: 1 notebook (first detection priority)
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- `keep_last`: 1 notebook (latest information)
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- `keep_most_complete`: 5 notebooks (preserve all details)
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- `keep_highest_confidence`: 2 notebooks (most reliable data)
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- `merge_all`: 3 notebooks (combine all information)
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### Conflict Detection Methods (6 total)
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- `value`: 4 notebooks (property value conflicts)
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- `type`: 2 notebooks (type/classification conflicts)
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- `entity`: 2 notebooks (entity-wide conflicts)
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- `relationship`: 3 notebooks (relationship conflicts)
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- `temporal`: 3 notebooks (time-sensitive conflicts)
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- `logical`: 2 notebooks (logical inconsistencies)
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### Conflict Resolution Strategies (6 total)
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- `voting`: 5 notebooks (majority vote)
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- `credibility_weighted`: 4 notebooks (source credibility)
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- `most_recent`: 3 notebooks (latest data)
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- `first_seen`: 1 notebook (first detection)
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- `highest_confidence`: 2 notebooks (most confident)
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- `expert_review`: 1 notebook (manual review)
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---
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## Key Patterns
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1. **Real-Time Systems**: Use `pairwise` + `keep_first` + `first_seen`
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2. **Time-Sensitive Data**: Use `temporal` + `most_recent`
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3. **Multi-Source Integration**: Use `batch` + `merge_all` + `voting`
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4. **Medical/Research**: Use `credibility_weighted` for authoritative sources
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5. **Fraud/Security**: Use `graph_based` + `logical` + `expert_review`
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6. **Exact Matching Required**: Use `exact` strategy (IOCs, identifiers)
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