Online windowed subsequence matching over probabilistic sequences

Abstract

Windowed subsequence matching over deterministic strings has been studied in previous work in the contexts of knowledge discovery, data mining, and molecular biology. However, we observe that in these applications, as well as in data stream monitoring, complex event processing, and time series data processing in which streams can be mapped to strings, the strings are often noisy and probabilistic. We study this problem in the online setting where efficiency is paramount. We first formulate the query semantics, and propose an exact algorithm. Then we propose a randomized approximation algorithm that is faster and, in the mean time, provably accurate. Moreover, we devise a filtering algorithm to further enhance the efficiency with an optimization technique that is adaptive to sequence stream contents. Finally, we propose algorithms for patterns with negations. In order to verify the algorithms, we conduct a systematic empirical study using three real datasets and some synthetic datasets.