Optimizing Complex Event Processing over RFID Data Streams

One research question crucial to RFID technology's wider adoption is how to efficiently transform sequences of RFID readings into meaningful business events. Contrary to traditional events, RFID readings are usually of high volume and velocity, and have the attributes representing their reading objects, occurrence times and spots. Based on these characteristics and the non-deterministic finite automata (NFA) implementation framework, this paper studies the performance issues of RFID complex event processing and proposes corresponding optimization techniques. Our techniques include : (1) taking advantage of negation events or exclusiveness between events to prune intermediate results, thus reduce memory consumption; (2) with complex events' different selectivities, purposefully reordering the join operations between events to improve overall efficiency, thus achieve higher stream throughput; (3) utilizing the slot-based or B+-tree-based approach to optimize the processing performance with the time window constraint. We present these techniques' analytical results and validate their effectiveness through experiments.