Discovering and exploiting keyword and attribute-value co-occurrences to improve P2P routing indices

  • Authors:
  • Sebastian Michel;Matthias Bender;Nikos Ntarmos;Peter Triantafillou;Gerhard Weikum;Christian Zimmer

  • Affiliations:
  • Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany;RACTI and University of Patras, Rio, Greece;RACTI and University of Patras, Rio, Greece;Max-Planck-Institut für Informatik, Saarbrücken, Germany;Max-Planck-Institut für Informatik, Saarbrücken, Germany

  • Venue:
  • CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
  • Year:
  • 2006

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Abstract

Peer-to-Peer (P2P) search requires intelligent decisions for query routing: selecting the best peers to which a given query, initiated at some peer, should be forwarded for retrieving additional search results. These decisions are based on statistical summaries for each peer, which are usually organized on a per-keyword basis and managed in a distributed directory of routing indices. Such architectures disregard the possible correlations among keywords. Together with the coarse granularity of per-peer summaries, which are mandated for scalability, this limitation may lead to poor search result quality.This paper develops and evaluates two solutions to this problem, sk-STAT based on single-key statistics only, and mk-STAT based on additional multi-key statistics. For both cases, hash sketch synopses are used to compactly represent a peer's data items and are efficiently disseminated in the P2P network to form a decentralized directory. Experimental studies with Gnutella and Web data demonstrate the viability and the trade-offs of the approaches.