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KaifAhmad1 8cde40d753 Remove trading notebooks and supply chain risk management notebook 
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Deduplication & Conflict Resolution Strategies Summary

Quick Reference by Use Case

Use Case Deduplication Method Merge Strategy Conflict Detection Conflict Resolution
Finance
01_Financial_Data_Integration_MCP DuplicateDetector (incremental) keep_highest_confidence temporal most_recent
02_Fraud_Detection ClusterBuilder (graph_based) merge_all logical expert_review
Biomedical
01_Drug_Discovery_Pipeline EntityResolver (semantic) - relationship voting
02_Genomic_Variant_Analysis DuplicateDetector (group) keep_most_complete value credibility_weighted
Cybersecurity
01_Real_Time_Anomaly_Detection DuplicateDetector (pairwise) keep_first entity first_seen
02_Threat_Intelligence_Hybrid_RAG EntityResolver (exact) - type highest_confidence
Blockchain
01_DeFi_Protocol_Intelligence DuplicateDetector (group) keep_last relationship voting
02_Transaction_Network_Analysis ClusterBuilder (hierarchical) keep_most_complete temporal most_recent
Intelligence
01_Criminal_Network_Analysis EntityResolver (fuzzy) - value credibility_weighted
02_Intelligence_Analysis_Orchestrator_Worker DuplicateDetector (batch) merge_all entity voting
Renewable Energy
01_Energy_Market_Analysis DuplicateDetector (pairwise) keep_highest_confidence temporal most_recent
Supply Chain
01_Supply_Chain_Data_Integration DuplicateDetector (incremental) keep_most_complete value credibility_weighted

Strategy Rationale by Domain

Finance

  • Financial Data Integration: Incremental for streaming data; most_recent for time-sensitive financial data
  • Fraud Detection: Graph-based clustering for fraud groups; expert_review for fraud assessment

Biomedical

  • Drug Discovery: Semantic matching for drug compounds; voting for research source aggregation
  • Genomic Variants: Group method for related variants; credibility weighting for research sources

Cybersecurity

  • Real-Time Anomaly: Pairwise for real-time streams; keep_first for first detection priority
  • Threat Intelligence: Exact matching for IOCs; highest_confidence for threat classification

Blockchain

  • DeFi Protocols: Group method for related protocols; keep_last for latest protocol info
  • Transaction Networks: Hierarchical clustering for nested groups; temporal for time-sensitive data

Intelligence

  • Criminal Networks: Fuzzy matching for intelligence data; credibility weighting for intelligence sources
  • Intelligence Analysis: Batch for multi-source integration; merge_all to combine all intelligence sources

Renewable Energy

  • Energy Markets: Pairwise for real-time market data; most_recent for time-sensitive energy data

Supply Chain

  • Supply Chain Integration: Incremental for continuous updates; credibility weighting for supply chain sources

Method Distribution

Deduplication Methods (9 total)

  • pairwise: 2 notebooks (real-time processing)
  • batch: 3 notebooks (large datasets)
  • incremental: 2 notebooks (streaming/continuous)
  • group: 2 notebooks (related entities)
  • graph_based (ClusterBuilder): 2 notebooks (interconnected entities)
  • hierarchical (ClusterBuilder): 1 notebook (nested groups)
  • exact (EntityResolver): 1 notebook (exact matching)
  • semantic (EntityResolver): 2 notebooks (semantic similarity)
  • fuzzy (EntityResolver): 1 notebook (fuzzy matching)

Merge Strategies (5 total)

  • keep_first: 1 notebook (first detection priority)
  • keep_last: 1 notebook (latest information)
  • keep_most_complete: 5 notebooks (preserve all details)
  • keep_highest_confidence: 2 notebooks (most reliable data)
  • merge_all: 3 notebooks (combine all information)

Conflict Detection Methods (6 total)

  • value: 4 notebooks (property value conflicts)
  • type: 2 notebooks (type/classification conflicts)
  • entity: 2 notebooks (entity-wide conflicts)
  • relationship: 3 notebooks (relationship conflicts)
  • temporal: 3 notebooks (time-sensitive conflicts)
  • logical: 2 notebooks (logical inconsistencies)

Conflict Resolution Strategies (6 total)

  • voting: 5 notebooks (majority vote)
  • credibility_weighted: 4 notebooks (source credibility)
  • most_recent: 3 notebooks (latest data)
  • first_seen: 1 notebook (first detection)
  • highest_confidence: 2 notebooks (most confident)
  • expert_review: 1 notebook (manual review)

Key Patterns

  1. Real-Time Systems: Use pairwise + keep_first + first_seen
  2. Time-Sensitive Data: Use temporal + most_recent
  3. Multi-Source Integration: Use batch + merge_all + voting
  4. Medical/Research: Use credibility_weighted for authoritative sources
  5. Fraud/Security: Use graph_based + logical + expert_review
  6. Exact Matching Required: Use exact strategy (IOCs, identifiers)
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