Abstract
Peer-to-peer applications are used to share large volumes of data. An important requirement of these systems is efficient methods for locating the data of interest in a large collection of data. Unfortunately current peer-to-peer systems either offer exact keyword match functionality or provide inefficient text search methods through centralized indexing or flooding. In this paper we propose a method based on popular Information Retrieval techniques to facilitate content-based searches in peer-to-peer systems. A simulation of the proposed design was implemented and its performance was evaluated using some commonly used test collections, including Ohsumed which was used for the TREC-9 Filtering Track. The experiments demonstrate that our approach is scalable as it achieves high recall by visiting only a small subset of the peers.
This research was funded in parts by NSF grants EIAÂ 00-80134, IISÂ 02-09112, and CNFÂ 04-23336.
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Sahin, O.D., Emekci, F., Agrawal, D., El Abbadi, A. (2005). Content-Based Similarity Search over Peer-to-Peer Systems . In: Ng, W.S., Ooi, BC., Ouksel, A.M., Sartori, C. (eds) Databases, Information Systems, and Peer-to-Peer Computing. DBISP2P 2004. Lecture Notes in Computer Science, vol 3367. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31838-5_5
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