Lineage-based probabilistic event stream processing

Many sensor network applications such as monitoring video camera streams or management of RFID data streams or tiny sensor data streams from Motes or SunSPOTs require the ability to detect composite events over high-volume data streams. Sensor data inputs from physical world are usually noisy, incomplete and unreliable because sensing devices are usually unreliable. Thus they are usually expressed with probability in ubiquitous sensor network environment. To manage this kind of data, the probabilistic event stream processing system is a natural consequence. In this paper, we propose a query language to support probabilistic queries for composite event stream matching. The language allows users to express Kleene closure patterns for complex event detection in physical world. We also propose a working framework for query processing over probabilistic event streams. Our method first detects sequence patterns over probabilistic data streams by using a new data structure, AIG which handles a record sets of active states with a NFA-based approach. After detecting active states, our method then computes the probability of each detected sequence pattern on its lineage. That is, query processing and confidence computation are decoupled. By the benefit of lineage, the probability of an output event can be directly calculated without considering the query plan. We conduct a performance evaluation of our method comparing with naive one which is called possible worlds approach. The result clearly shows the effectiveness of our approach. While our approach shows scalable throughput, naive approach degrades its performance rapidly. The experiments are conducted with the window size, the number of event types and the number of alternatives.