CaseLinker - System Architecture

Case Reports

Structured Case Reports / Case Related Data

Input documents containing case narratives, investigation reports, and case-related data as PDFs.

Layer 1

Ingestion

Text Extraction & Normalization

# Extract text from PDF

text = extract_pdf_text("case_report.pdf")

# Normalize format

normalized = normalize_text(text)

# Returns: Clean, structured text ready for processing

PDF text extraction using pdfplumber
Organization detection from filenames
Batch processing support
Input validation and error handling

Layer 2

Processing

Feature Extraction & Case Schema

How it's done: Hybrid approach using regex patterns for structured data (demographics, platforms, evidence, prosecution) and pattern-based matching for semantic features (severity indicators, case topics, severity phrases). ML/NER extraction supplements with law enforcement agencies, ages, dates, and locations. Text is cleaned (URL removal, artifact normalization), cases are batched by temporal patterns, and unique case IDs are generated.

# 1. Clean URLs and artifacts

cleaned_text = clean_urls_from_text(raw_text)

# 2. Batch cases by month patterns

cases = case_batching(cleaned_text, org_name="azicac")

# 3. Extract features (regex + patterns + NER)

features = extract_features(case)

# Regex: Demographics, platforms, evidence, prosecution

# Patterns: Severity indicators, case topics, phrases

# NER: Law enforcement agencies, ages, dates, locations

Demographics

Victim age, count, gender
Perpetrator age, RSO status

Platforms

Social media, online methods, communication channels

Severity

Infant, very young, rape, production indicators

Topics

Hands-on, possession, online-only, family, stranger

Prosecution

Charges, booking status, outcomes

Evidence

Images, videos, storage volume, messages

Layer 3

Storage

Case Schema & Database Storage

# Store case with schema

storage = CaseStorage("caselinker.db")

storage.store_case(case)

# Normalized tables:

# - cases (main table)

# - victim_demographics

# - perpetrator_demographics

# - prosecution_outcomes

# - Raw data preserved in JSON

SQLite database with normalized schema
Raw data preservation in JSON format
Indexed fields for fast queries
Modular interface for database swapping

Layer 4

Analysis

Tag-Based Filtering & Triage

# Tag-based filtering (intersection logic)

# Select case features to analzye across categories:

# - Case Topics

# - Severity Indicators

# - Platforms & Environments

# - Investigation Types

# - Perpetrator Relationships

# - Custom topics

cases = return_tagged_cases(all_cases, [

  {'tag': 'production', 'category': 'case_topics'},

  {'tag': 'infant', 'category': 'severity_indicators'},

  {'tag': 'facebook', 'category': 'platforms_used'},

])

# Returns: Cases matching ALL selected tags

# Priority triage scoring

triaged = triage_cases(cases)

# Multi-factor scoring (5-10 scale):

# - Severity (35%), Victim count (30%)

# - Case type (25%), Evidence (10%), RSO (10%)

Tag-based filtering with intersection logic
Select cases by common features/tags
Multi-factor priority triage (5-10 scale)
Automated pattern detection and insights

Layer 5

Visualization

Interactive Visualizations & Analyst Interface

# D3.js visualizations

renderTimeline(cases)

renderSeverityViz(cases)

renderOutcomesViz(cases)

renderAutomatedAnalysis(results)

# Interactive features:

# - Click to view case details

# - Text highlighting for verification

# - Temporal filtering

# - Case grouping visualization

Timeline visualization (chronological view)
Severity indicators (color-coded analysis)
Prosecution outcomes (categorized display)
Perpetrator demographics (pie charts)
Platform/environment distribution
Organizations involved (agency analysis)