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SWIFT: Mining Representative Patterns from Large Event Streams

Author(s)
Yan, Yizhou; Cao, Lei; Madden, Samuel; Rundensteiner, Elke A.
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Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/
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Abstract
© 2018 VLDB Endowment 21508097/18/07. Event streams generated by smart devices common in modern Internet of Things applications must be continuously mined to monitor the behavior of the underlying system. In this work, we propose a stream pattern mining system for supporting online IoT applications. First, to solve the pattern explosion problem of existing stream pattern mining strategies, we now design pattern semantics that continuously produce a compact set of patterns that maximumly compresses the dynamic data streams, called MDL-based Representative Patterns (MRP). We then design a one-pass SWIFT approach that continuously mines the up-to-date MRP pattern set for each stream window upon the arrival or expiration of individual events. We show that SWIFT is guaranteed to select the update operation for each individual incoming event that leads to the most compact encoding of the sequence in the current window. We further enhance SWIFT to support batch updates, called B-SWIFT. BSWIFT adopts a lazy update strategy that guarantees that only the minimal number of operations are conducted to process an incoming event batch for MRP pattern mining. Evaluation by our industry lighting lab collaborator demonstrates that SWIFT successfully solves their use cases and finds more representative patterns than the alternative approaches adapting the state-of-the-art static representative pattern mining methods. Our experimental study confirms that SWIFT outperforms the best existing method up to 50% in the compactness of produced pattern encodings, while providing a 4 orders of magnitude speedup.
Date issued
2018-11
URI
https://hdl.handle.net/1721.1/137804
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Journal
Proceedings of the VLDB Endowment
Publisher
VLDB Endowment
Citation
Yan, Yizhou, Cao, Lei, Madden, Samuel and Rundensteiner, Elke A. 2018. "SWIFT: Mining Representative Patterns from Large Event Streams." Proceedings of the VLDB Endowment, 12 (3).
Version: Final published version
ISSN
2150-8097

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