Peer-to-peer similarity search based on m-tree indexing

Authors:
Akrivi Vlachou;Christos Doulkeridis;Yannis Kotidis
Affiliations:
Dept. of Computer and Information Science, Norwegian University of Science and Technology;Dept. of Computer and Information Science, Norwegian University of Science and Technology;Dept. of Informatics, Athens University of Economics and Business
Venue:
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Year:
2010

Citing 9
Cited 2

Searching in metric spaces

ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces

VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)

ACM Transactions on Database Systems (TODS)
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search

ACM Transactions on Database Systems (TODS)
M-Chord: a scalable distributed similarity search structure

InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Peer-to-peer similarity search in metric spaces

VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Designing a Peer-to-Peer Architecture for Distributed Image Retrieval

Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Efficient range query processing in metric spaces over highly distributed data

Distributed and Parallel Databases
A content-addressable network for similarity search in metric spaces

DBISP2P'05/06 Proceedings of the 2005/2006 international conference on Databases, information systems, and peer-to-peer computing

Distributed similarity estimation using derived dimensions

The VLDB Journal — The International Journal on Very Large Data Bases
Metric-Based similarity search in unstructured peer-to-peer systems

Transactions on Large-Scale Data- and Knowledge-Centered Systems V

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Abstract

Similarity search in metric spaces has several important applications both in centralized and distributed environments. In centralized applications, such as similarity-based image retrieval, usually a server indexes its data with a state-of-the-art centralized metric indexing technique, such as the M-Tree. In this paper, we propose a framework for distributed similarity search, where each participating peer stores its own data autonomously, under the assumption that data is indexed locally by peers using M-Trees. In order to support scalability and efficiency of search, we adopt a super-peer architecture, where super-peers are responsible for query routing. We propose the construction of metric routing indices suitable for distributed similarity search in metric spaces. We study the performance of the proposed framework using both synthetic and real data.