Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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Recursive hashing functions for n-grams
ACM Transactions on Information Systems (TOIS)
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Efficient processing of subsequence matching with the Euclidean metric in time-series databases
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Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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Ranked subsequence matching in time-series databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Continuous time-series sequence matching, specifically, matching a numeric live stream against a set of redefined pattern sequences, is critical for domains ranging from fire spread tracking to network traffic monitoring. While several algorithms exist for similarity matching of static time-series data, matching continuous data poses new, largely unsolved challenges including online real-time processing requirements and system resource limitations for handling infinite streams. In this work, we propose a novel live stream matching framework, called n-Snippet Indices Framework (in short, SNIF), to tackle these challenges. SNIF employs snippets as the basic unit for matching streaming time-series. The insight is to perform the matching at two levels of granularity: bag matching of subsets of snippets of the live stream against prefixes of the patterns, and order checking for maintaining successive candidate snippet bag matches. We design a two-level index structure, called SNIF index, which supports these two modes of matching. We propose a family of online two-level prefix matching algorithms that trade off between result accuracy and response time. The effectiveness of SNIF to detect patterns has been thoroughly tested through experiments using real datasets from the domains of fire monitoring and sensor motes. In this paper, we also present a study of SNIF's performance, accuracy and tolerance to noise compared against those of the state-of-the-art Continuous Query with Prediction (CQP) approach.