Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
GlOSS: text-source discovery over the Internet
ACM Transactions on Database Systems (TODS)
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Chord: A scalable peer-to-peer lookup service for internet applications
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
Super-peer-based routing and clustering strategies for RDF-based peer-to-peer networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
An extensible meta-learning approach for scalable and accurate inductive learning
An extensible meta-learning approach for scalable and accurate inductive learning
Bayesian models for visual information retrieval
Bayesian models for visual information retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
MINERVA: collaborative P2P search
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Scalable summary based retrieval in P2P networks
Proceedings of the 14th ACM international conference on Information and knowledge management
PRISM: indexing multi-dimensional data in P2P networks using reference vectors
Proceedings of the 13th annual ACM international conference on Multimedia
Clustering-Based Source Selection for Efficient Image Retrieval in Peer-to-Peer Networks
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Source selection for image retrieval in peer-to-peer networks
FDIA'09 Proceedings of the Third BCS-IRSG conference on Future Directions in Information Access
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In peer-to-peer (P2P) networks, computers with equal rights form a logical (overlay) network in order to provide a common service that lies beyond the capacity of every single participant. Efficient similarity searchis generally recognized as a frontier in research about P2P systems. In literature, a variety of approaches exist. One of which is data source selection based approaches where peers summarize the data they contribute to the network, generating typically one summary per peer. When processing queries, these summaries are used to choose the peers (data sources) that are most likely to contribute to the query result. Only those data sources are contacted.In this paper we use a Gaussian mixture model to generate peer summaries using the peers' local data. We compare this method to other local unsupervised clustering methods for generating peer summaries and show that a Gaussian mixture model is promising when it comes to locally generated summaries for peers without the need for a distributed summary computation that needs coordination between peers.