Efficient multiple objects-oriented event detection over RFID data streams

Authors:
Shanglian Peng;Zhanhuai Li;Qiang Li;Qun Chen;Hailong Liu;Yanming Nie;Wei Pan
Affiliations:
School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China;College of Software and Microelectronics, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an, China
Venue:
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Year:
2010

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Abstract

Complex event processing has been extensively applied in areas such as RFID tracking for supply chain management, fluctuation detection in stock trading, real-time intrusion detection in network monitoring, etc. Most existing research works focus on specification, formalization and evaluation of single-object oriented complex event processing. In this paper, we investigate complex event processing problems over multiple correlated RFID objects. We study multiple correlated RFID event detection problems. We present two kinds of evaluation algorithms: SEquence Join Algorithm(SEJA) and Stream Join Algorithm(SJA). Experimental studies demonstrate that our proposed algorithms are effective and scalable.