The Community for Technology Leaders
RSS Icon
Subscribe
Atlanta, GA, USA
April 3, 2006 to April 7, 2006
ISBN: 0-7695-2570-9
pp: 41
Philippe Cudre-Mauroux , School of Computer and Communication Sciences EPFL, Switzerland
Karl Aberer , School of Computer and Communication Sciences EPFL, Switzerland
Andras Feher , Fachbereich Informatik T.U. Darmstadt, Germany
ABSTRACT
Until recently, most data integration techniques involved central components, e.g., global schemas, to enable transparent access to heterogeneous databases. Today, however, with the democratization of tools facilitating knowledge elicitation in machine-processable formats, one cannot rely on global, centralized schemas anymore as knowledge creation and consumption are getting more and more dynamic and decentralized. Peer Data Management Systems (PDMS) provide an answer to this problem by eliminating the central semantic component and considering instead compositions of local, pair-wise mappings to propagate queries from one database to the others. <p>PDMS approaches proposed so far make the implicit assumption that all mappings used in this way are correct. This obviously cannot be taken as granted in typical PDMS settings where mappings can be created (semi) automatically by independent parties. In this work, we propose a totally decentralized, efficient message passing scheme to automatically detect erroneous mappings in PDMS. Our scheme is based on a probabilistic model where we take advantage of transitive closures of mapping operations to confront local belief on the correctness of a mapping against evidences gathered around the network. We show that our scheme can be efficiently embedded in any PDMS and provide a preliminary evaluation of our techniques on sets of both automatically-generated and real-world schemas.</p>
INDEX TERMS
null
CITATION
Philippe Cudre-Mauroux, Karl Aberer, Andras Feher, "Probabilistic Message Passing in Peer Data Management Systems", ICDE, 2006, 22nd International Conference on Data Engineering, 22nd International Conference on Data Engineering 2006, pp. 41, doi:10.1109/ICDE.2006.118
19 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool