Exploiting latent relevance for relational learning of ubiquitous things
Proceedings of the 21st ACM international conference on Information and knowledge management
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With recent advances in technologies such as radio-frequency identification and new standards such as the electronic product code, large-scale traceability is emerging as a key differentiator in a wide range of enterprise applications (e.g. counterfeit prevention, product recalls and pilferage reduction). Such traceability applications often need to access data collected by individual enterprises in a distributed environment. Traditional centralized approaches (e.g. data warehousing) are not feasible for these applications due to their unique characteristics such as large volume of data and sovereignty of the participants. In this paper, we describe an approach that enables applications to share traceability data across independent enterprises in a pure peer-to-peer (P2P) fashion. Data are stored in local repositories of participants and indexed in the network based on structured P2P overlays. In particular, we present a generic approach for efficiently indexing and locating individual objects in large, distributed traceable networks, most notably, in the emerging environment of the internet of things. The results from extensive experiments show that our approach scales well in both data volume and network size. A real-world returnable assets management system is also developed using the proposed techniques to demonstrate its feasibility.