TY - GEN
T1 - Cost-conscious cleaning of massive RFID data sets
AU - Gonzalez, Hector
AU - Han, Jiawei
AU - Shen, Xuehua
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Efficient and accurate data cleaning is an essential task for the successful deployment of RFID systems. Although important advances have been made in tag detection rates, it is still common to see a large number of lost readings due to radio frequency (RF) interference and tag-reader configurations. Existing cleaning techniques have focused on the development of accurate methods that work well under a wide set of conditions, but have disregarded the very high cost of cleaning in a real application that may have thousands of readers and millions of tags. In this paper, we propose a cleaning framework that takes an RFID data set and a collection of cleaning methods, with associated costs, and induces a cleaning plan that optimizes the overall accuracy-adjusted cleaning costs by determining the conditions under which inexpensive methods are appropriate, and those under which more expensive methods are absolutely necessary.
AB - Efficient and accurate data cleaning is an essential task for the successful deployment of RFID systems. Although important advances have been made in tag detection rates, it is still common to see a large number of lost readings due to radio frequency (RF) interference and tag-reader configurations. Existing cleaning techniques have focused on the development of accurate methods that work well under a wide set of conditions, but have disregarded the very high cost of cleaning in a real application that may have thousands of readers and millions of tags. In this paper, we propose a cleaning framework that takes an RFID data set and a collection of cleaning methods, with associated costs, and induces a cleaning plan that optimizes the overall accuracy-adjusted cleaning costs by determining the conditions under which inexpensive methods are appropriate, and those under which more expensive methods are absolutely necessary.
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U2 - 10.1109/ICDE.2007.368990
DO - 10.1109/ICDE.2007.368990
M3 - Conference contribution
AN - SCOPUS:34548740151
SN - 1424408032
SN - 9781424408030
T3 - Proceedings - International Conference on Data Engineering
SP - 1268
EP - 1272
BT - 23rd International Conference on Data Engineering, ICDE 2007
T2 - 23rd International Conference on Data Engineering, ICDE 2007
Y2 - 15 April 2007 through 20 April 2007
ER -