Data Capture Systems

Framework for decentralized data capture and verification in AI systems.

Data Capture Systems

Capture Mechanisms

Web Proofs

const WebProofTypes = {
  identity: "Verified credentials",
  activity: "Platform interactions",
  reputation: "Trust scores",
  ownership: "Asset holdings"
}

Data Sources

  • Social networks
  • Marketplaces
  • Financial services
  • Gaming platforms

Verification Systems

Proof Mechanisms

  1. Zero-knowledge proofs
  2. Trusted execution environments
  3. Multi-party computation
  4. Oracle networks

Validation Framework

const ValidationProcess = {
  collection: "Raw data ingestion",
  verification: "Proof generation",
  attestation: "Third-party validation",
  storage: "Distributed storage"
}

Data Quality

Quality Metrics

  • Accuracy
  • Completeness
  • Timeliness
  • Consistency

Quality Control

  1. Automated validation
  2. Peer review
  3. Reputation systems
  4. Economic incentives

System Architecture

Components

  1. Data collectors
  2. Verification nodes
  3. Storage providers
  4. Access control

Integration Points

  • API endpoints
  • Event streams
  • Query interfaces
  • Proof verification

"Robust data capture systems form the foundation for trustworthy AI training and inference."