Lineage-based Probabilistic Event Stream Processing

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.