Abstract
RFID technology provides significant advantages over traditional object-tracking technologies and is increasingly adopted and deployed in real applications. RFID applications generate large volume of streaming data, which have to be automatically filtered, processed, and transformed into semantic data, and integrated into business applications. Indeed, RFID data are highly temporal, and RFID observations form complex temporal event patterns which can be very different for various RFID applications. Thus, it is desirable to have a general RFID data processing framework with a powerful language, for the end users to express a variety of queries on RFID data streams, as well as detecting complex events patterns. While data stream management systems (DSMSs) are emerging for optimized stream data processing, they usually lack the language construct support for temporal event detection. In this paper, we discuss a stream query language to provide comprehensive temporal event detection, through temporal operators and extension of sliding-window constructs. With the integration of temporal event detection, a DSMS has the capability to serve as a powerful system for RFID data processing.
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