Scalable Self-Organizing Structured P2P Information Retrieval Model Based on Equivalence Classes

This paper proposes a new autonomous self-organizing content-based node clustering peer to peer Information Retrieval (P2PIR) model. This model uses incremental transitive document-to-document similarity technique to build Local Equivalence Classes (LECes) of documents on a source node. Locality Sensitive Hashing (LSH) scheme is applied to map a representative of each LEC into a set of keys which will be published to hosting node(s). Similar LECes on different nodes form Universal Equivalence Classes (UECes), which indicate the connectivity between these nodes. The same LSH scheme is used to submit queries to subset of nodes that most likely have relevant information. The proposed model has been implemented. The obtained results indicate efficiency in building connectivity between similar nodes, and correctly allocate and retrieve relevant answers to high percentage of queries. The system was tested for different network sizes and proved to be scalable as efficiency downgraded gracefully as the network size grows exponentially.