TRAC

OriginTrail (TRAC) addresses the pervasive issue of misinformation, fragmented data, and lack of verifiable truth in both digital and real-world contexts. It tackles challenges like publication bias in scientific research, supply chain opacity, and the need for trustworthy data to empower AI. By providing a Decentralized Knowledge Graph (DKG), OriginTrail ensures data's origin, ownership, and discoverability are verifiable, enhancing reliability and fostering collaboration across industries like healthcare, manufacturing, and construction, enabling safer and more transparent operations.

The TRAC token powers the OriginTrail Decentralized Knowledge Graph (DKG) network operations. Its primary use cases include incentivizing network participants: Asset publishers use TRAC to compensate node runners for data replication and discoverability of Knowledge Assets. Node runners stake TRAC as collateral, increasing their chances of earning fees for hosting DKG segments. Furthermore, token holders can delegate their TRAC to nodes to earn rewards, collectively securing the network and ensuring the integrity of verifiable knowledge.

OriginTrail implements zero-knowledge proofs (ZKPs) for sensitive data verification without disclosure. Participants prove compliance with requirements like safety standards or audit conditions without revealing underlying confidential information. For public verification, cryptographic hashes of knowledge assets are anchored to blockchains, enabling tamper detection while keeping raw data off-chain. This combines selective disclosure with blockchain-backed integrity checks.

Unlike centralized databases, the DKG enables multi-stakeholder data sharing without single-point control. Participants retain data ownership while establishing cryptographic proof relationships between assets. For example: a factory can prove certification status to retailers without granting full database access; logistics providers can verify shipment conditions without seeing proprietary costing data. This breaks information silos while preserving commercial confidentiality through semantic interoperability standards.

Operators earn TRAC through three primary mechanisms: 1) Publishing fees from entities adding data to the DKG, distributed proportionally to nodes hosting that data; 2) Staking rewards via the Random Sampling system where nodes with higher stakes receive more publishing assignments; 3) Delegation shares where token holders delegate TRAC to nodes in exchange for reward distribution. Rewards derive solely from usage fees without inflationary issuance.

Yes, through standardized adapters supporting common protocols. The DKG Edge Node provides lightweight integration for devices like IoT sensors, translating proprietary data into semantic formats. For ERP systems, OriginTrail offers GS1-compliant data modules that map commercial transactions to knowledge assets. Major implementations include SAP integration for SBB's rail operations and Oracle Cloud compatibility for supply chain partners.

The Convergence period introduces autonomous knowledge inferencing where the DKG automatically identifies relationships between assets across domains. This enables: 1) Cross-industry insights (e.g., material shortages affecting medical supplies); 2) dRAG (decentralized Retrieval-Augmented Generation) providing auditable sources for AI outputs; 3) Neuro-symbolic AI agents combining LLMs with verified knowledge graphs. These developments create network effects as more interconnected assets increase contextual intelligence.