Content-Addressed Subscription: A Scalable Matching Mechanism for DAO-Governed Industrial Data Market

Abstract

The realization of Industry 5.0 depends on the effective utilization of high-quality, context-rich, long-tail data. Existing centralized data markets are inefficient due to high operational costs and persistent data silos. As a novel autonomous paradigm based on block-chain and smart contracts, Decentralized Autonomous Organization (DAO) offers a superior governance framework to address these issues. However, DAO natively lacks efficient data discovery mechanisms. The prevailing pull-based query model is economically unviable for large-scale, fine-grained demands due to excessive on-chain costs. To tackle this challenge, this paper introduces Content-Addressed Subscription, a novel data discovery mechanism operating within a DAO-governed industrial data market. This mechanism builds upon the classic publish/subscribe model, algorithmically generating a unique topic identifier from the semantic content, thereby circumventing the reliance on predefined topic lists and transforming the data discovery process from inefficient pull-based queries to event-driven push notifications. The paper presents the complete architecture of the proposed solution and validates its feasibility through an industrial case study.