Peer-to-Peer Networking in Mobile Communications Based on Sip

Huber, Josef F.

doi:10.1007/0-306-48642-3_1

Cited by 1 publication

(1 citation statement)

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“…Finding a fair balance between give and take among peers may be crucial to the success of this technology. [164] There are a number of books published about Peer-to-Peer systems [138,123,68,54,128,17,169,100], mobile networks [140,85], Peer-to-Peer Information Retrieval [see 7,173]. Good surveys of Peer-to-Peer systems are [124,11].…”

Section: Peer-to-peer Systemsmentioning

confidence: 99%

A Content–Addressable Network for Similarity Search in Metric Spaces

Falchi

Gennaro

Zezula

Databases, Information Systems, and Peer-to-Peer Computing

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Because of the ongoing digital data explosion, more advanced search paradigms than the traditional exact match are needed for contentbased retrieval in huge and ever growing collections of data produced in application areas such as multimedia, molecular biology, marketing, computer-aided design and purchasing assistance. As the variety of data types is fast going towards creating a database utilized by people, the computer systems must be able to model human fundamental reasoning paradigms, which are naturally based on similarity. The ability to perceive similarities is crucial for recognition, classification, and learning, and it plays an important role in scientific discovery and creativity. Recently, the mathematical notion of metric space has become a useful abstraction of similarity and many similarity search indexes have been developed. In this thesis, we accept the metric space similarity paradigm and concentrate on the scalability issues. By exploiting computer networks and applying the Peer-to-Peer communication paradigms, we build a structured network of computers able to process similarity queries in parallel. Since no centralized entities are used, such architectures are fully scalable. Specifically, we propose a Peerto-Peer system for similarity search in metric spaces called Metric Content-Addressable Network (MCAN) which is an extension of the well known Content-Addressable Network (CAN) used for hash lookup. A prototype implementation of MCAN was tested on real-life datasets of image features, protein symbols, and text-observed results are reported. We also compared the performance of MCAN with three other, recently proposed, distributed data structures for similarity search in metric spaces. It is a great honor and privilege for me to have an international joint supervision of my Ph.D. dissertation. I want to thank the University of Pisa and the Masaryk University, Brno for their agreement regarding this joint supervision. I am grateful to my supervisors Lanfranco Lopriore, Fausto Rabitti and Pavel Zezula for their guidance and encouragement. Since I was not just a Ph.D. student at the University of Pisa but also at the Masaryk University, I had the opportunity to work with several researchers in Brno, especially Michal Batko, David Novak and Vlastislav Dohnal. The discussions we had and the work we did together were fundamental for the completion of this thesis. This thesis would not have been possible without the substantial help from Claudio Gennaro and without the discussions I had with Giuseppe Amato and Pasquale Savino. I also thank all the other people at the Networked Multimedia Information Systems laboratory of the Institute of Information Science and Technologies, especially Fausto Rabitti who is the head of the laboratory and his predecessor Costantino Thanos. I would also like to thank Cosimo Antonio Prete and Sandro Bartolini for teaching me the basics of research during my master thesis. Finally, I thank my mother and father for supporting me during my studies.

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