Premium
Using semantic links to support top‐ K join queries in peer‐to‐peer networks
Author(s) -
Liu Jie,
Feng Liang,
Zhuge Hai
Publication year - 2007
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1145
Subject(s) - computer science , tuple , join (topology) , pruning , ranking (information retrieval) , peer to peer , rank (graph theory) , matching (statistics) , set (abstract data type) , information retrieval , value (mathematics) , data mining , node (physics) , theoretical computer science , distributed computing , machine learning , mathematics , statistics , structural engineering , discrete mathematics , combinatorics , agronomy , biology , programming language , engineering
An important issue raised in peer‐to‐peer (P2P) applications is how to accurately and efficiently retrieve a set of K best matching data objects from different sources while minimizing the number of objects that have to be accessed. The proposed solution is to organize peers by a semantic link network representing the semantic relationships between peers' data schemas. Queries are only routed to semantically relevant peers. A pruning‐based local top‐ K ranking approach is proposed to reduce the transmitted data by pruning tuples that cannot produce the desired join results with a rank value at least equal to the lowest rank value generated. Experiments evaluate its performance in terms of the number of transmitted tuples and the miss rate. Comparison with the traditional threshold algorithm for centralized systems and other top‐ K ranking algorithms for P2P networks shows the features of the proposed approach. Copyright © 2006 John Wiley & Sons, Ltd.