Interval-based Queries over Multiple Streams with Missing Timestamps

Recognising patterns that correlate multiple events over time becomes increasingly important in applications from urban transportation to surveillance monitoring. In many realworld scenarios, however, timestamps of events may be erroneously recorded and events may be dropped from a stream due to network failures or load shedding policies. In this work, we present SimpMatch, a novel simplex-based algorithm for probabilistic evaluation of event queries using constraints over event orderings in a stream. Our approach avoids learning probability distributions for time-points or occurrence intervals. Instead, we employ the abstraction of segmented intervals and compute the probability of a sequence of such segments using the principle of order statistics. The algorithm runs in linear time to the number of missed timestamps, and shows high accuracy, yielding exact results if event generation is based on a Poisson process and providing a good approximation otherwise. As we demonstrate empirically, SimpMatch enables efficient and effective reasoning over event streams, outperforming state-ofthe-art methods for probabilistic evaluation of event queries by up to two orders of magnitude.