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Adaptive cleaning for RFID data streams

Shawn R. Jeffery, Garofalakis Minos, Franklin Michael J.

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URIhttp://purl.tuc.gr/dl/dias/CC96CE04-CBF2-4254-99E9-60E54877435C-
Identifierhttp://www.vldb.org/conf/2006/p163-jeffery.pdf-
Languageen-
Extent12 pagesen
TitleAdaptive cleaning for RFID data streamsen
CreatorShawn R. Jefferyen
CreatorGarofalakis Minosen
CreatorΓαροφαλακης Μινωςel
CreatorFranklin Michael J.en
PublisherAssociation for Computing Machineryen
Content SummaryTo compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a “smoothing filter”, a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-12-01-
Date of Publication2006-
SubjectDatabases technologyen
Bibliographic CitationS. R. Jeffery, M. Garofalakis and M. J. Franklin, "Adaptive cleaning for RFID data streams", in 32nd International Conference on Very Large Data Bases, 2006, pp 163-174. en

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