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PROCESSING.md

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Data Processing Pipeline

Overview

The DataHive processing pipeline handles the transformation of raw legal data into structured, validated knowledge through a series of coordinated operations. This system works in conjunction with the indexing, curation, and validation modules.

Core Components

Processing Engine

class ProcessingEngine:
    def __init__(self):
        self.document_processor = DocumentProcessor()
        self.pattern_analyzer = PatternAnalyzer()
        self.knowledge_extractor = KnowledgeExtractor()

    async def process_document(self, legal_data):
        parsed = await self.document_processor.parse(legal_data)
        patterns = self.pattern_analyzer.detect(parsed)
        return self.knowledge_extractor.extract(patterns)

Processing Stages

Document Intake

  • Raw document collection
  • Format detection
  • Initial preprocessing
  • Quality assessment

Content Analysis

  • Text extraction
  • Structure analysis
  • Metadata generation
  • Reference detection

Knowledge Extraction

  • Legal concept identification
  • Relationship mapping
  • Citation extraction
  • Context analysis

Integration Points

Indexing System

  • Document classification
  • Content categorization
  • Reference mapping
  • Version tracking

Validation Layer

  • Quality verification
  • Accuracy checks
  • Consistency validation
  • Error detection

Storage System

  • Data persistence
  • Version control
  • Change tracking
  • Backup management

Quality Controls

Performance Metrics

  • Processing speed
  • Accuracy rates
  • Error frequency
  • Resource utilization

Quality Assurance

  • Automated testing
  • Manual review triggers
  • Performance monitoring
  • Continuous improvement

Note: This documentation is subject to updates as the processing system evolves.