Efficient Complex Event Processing over RFID Data Stream

RFID technology holds the promise of real-time identifying, locating and monitoring physical objects. To achieve these goals, RFID events need to be collected efficiently and composed expressively. Furthermore, these events have unique characteristics, such as locomotive, temporal and history oriented which should be considered and integrated into an event engine model. The diversity of RFID applications poses further challenges to a generalized framework for RFID events processing. In this paper, the Expressive Stream Language is utilized to collect vast number of primitive events efficiently. Moreover, we introduce a novel semantics to meet requirement of expressive event composition. At last, we use Timed Petri Net to model our newly RFID complex event engine. By introducing typical applications scenarios, we evaluate the validity and effectiveness of our RFID event processing system.