Abstract
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 search is 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.
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References
Bender, M., Michel, S., Triantafillou, P., Weikum, G., Zimmer, C.: Minerva: collaborative P2P search. In: VLDB 2005: Proc. of the 31st Intl. Conf. on Very large data bases. VLDB Endowment, pp. 1263–1266 (2005)
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Communications of the ACM 13(7) (1970)
Callan, J.P., Lu, Z., Croft, W.B.: Searching distributed collections with inference networks. In: Proc. 18th ACM SIGIR, Seattle, Washington (1995)
Chan, P.K.-W.: An extensible meta-learning approach for scalable and accurate inductive learning. PhD thesis, Sponsor-Salvatore J. Stolfo (1996)
Clarke, I., Sandberg, O., Wiley, B., Hong, T.W.: Freenet: A distributed anonymous information storage and retrieval system. In: Federrath, H. (ed.) Designing Privacy Enhancing Technologies. LNCS, vol. 2009. Springer, Heidelberg (2001)
Cuenca-Acuna, F.M., Nguyen, T.: Text-based content search and retrieval in ad hoc P2P communities. Technical Report DCS-TR-483, Department for Computer Science, Rutgers University (2002)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. Wiley-Interscience (2001)
Eisenhardt, M., Müller, W., Henrich, A.: Classifying documents by distributed P2P clustering, 286–291 (2003)
Eisenhardt, M., Müller, W., Henrich, A., Blank, D., El Allali, S.: Clustering-based source selection for efficient image retrieval in peer-to-peer networks. In: IEEE MIPR 2007, pp. 823–830 (2006)
El Allali, S., Blank, D., Eisenhardt, M., Henrich, A., Müller, W.: Untersuchung des Einflusses verschiedener Bild-Features und Distanzmaße im inhaltsbasierten P2P Information Retrieval. In: BTW 2007, 12th GI-Fachtagung für Datenbanksysteme in Business, Technologie und Web (2007)
Gravano, L., García-Molina, H., Tomasic, A.: Gloss: text-source discovery over the internet. ACM Trans. Database Syst. 24(2), 229–264 (1999)
Kronfol, A.Z.: A Fault-tolerant, Adaptive, Scalable, Distributed Search Engine. Final Thesis, Princeton (May 2002), http://www.searchlore.org/library/kronfol_final_thesis.pdf
Müller, W., Eisenhardt, M., Henrich, A.: Scalable summary based retrieval in P2P networks. In: CIKM 2005: Proc. of the 14th ACM Intl. Conf. on Information and knowledge management, pp. 586–593. ACM Press, New York (2005)
Müller, W., Henrich, A., Eisenhardt, M.: Aspects of adaptivity in P2P information retrieval. In: The 4th International Workshop on Adaptive Multimedia Retrieval AMR 2006 (2006)
Nejdl, W., Wolpers, M., Siberski, W., Schmitz, C., Schlosser, M., Brunkhorst, I., Löser, A.: Super-peer-based routing and clustering strategies for rdf-based peer-to-peer networks. In: Proc. of the Intl. World Wide Web Conf. (2003)
Qian, F., Li, M., Zhang, L., Zhang, H.-J., Zhang, B.: Gaussian mixture model for relevance feedback in image retrieval. In: IEEE International Conference on Multimedia and Expo, 2002. ICME 2002 (2002)
Ratnasamy, S., Francis, P., Handley, M., Karp, R., Schenker, S.: A scalable content-addressable network. In: Proc. 2001 Conf. on applications, technologies, architectures, and protocols for computer communications, San Diego, CA, United States (2001)
Sahin, O.D., Gulbeden, A., Emekci, F., Agrawal, D., Abbadi, A.E.: PRISM: indexing multi-dimensional data in P2P networks using reference vectors. In: Proc. of the 13th annual ACM Intl. Conf. on Multimedia, pp. 946–955. ACM Press, New York (2005)
Stoica, I., Morris, R., Karger, D., Kaashoek, F., Balakrishnan, H.: Chord: A scalable Peer-To-Peer lookup service for internet applications. In: Proc. ACM SIGCOMM Conf., San Diego, CA, USA (2001)
Tang, C., Xu, Z., Mahalingam, M.: pSearch: Information retrieval in structured overlays. In: First Workshop on Hot Topics in Networks (HotNets-I). Princeton, NJ (2002)
Vasconcelos, N.: Bayesian Models for Visual Information Retrieval. PhD thesis, MIT (June 2000)
Yang, B., Garcia-Molina, H.: Designing a super-peer network. In: IEEE Intl. Conf. on Data Engineering (2003)
Zhang, L., Lin, F., Zhang, B.: A cbir method based on color-spatial feature. In: IEEE Region 10 Annual International Conference 1999, pp. 166–169 (1999)
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El Allali, S., Blank, D., Müller, W., Henrich, A. (2008). Image Data Source Selection Using Gaussian Mixture Models. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds) Adaptive Multimedia Retrieval: Retrieval, User, and Semantics. AMR 2007. Lecture Notes in Computer Science, vol 4918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79860-6_14
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DOI: https://doi.org/10.1007/978-3-540-79860-6_14
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