research-article

T-Rex: Optimizing Pattern Search on Time Series

Authors:

Surajit Chaudhuri,

Leonhard SpiegelbergAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 130, Pages 1 - 26

https://doi.org/10.1145/3589275

Published: 20 June 2023 Publication History

Abstract

Pattern search is an important class of queries for time series data. Time series patterns often match variable-length segments with a large search space, thereby posing a significant performance challenge. The existing pattern search systems, for example, SQL query engines supporting MATCH_RECOGNIZE, are ineffective in pruning the large search space of variable-length segments. In many cases, the issue is due to the use of a restrictive query language modeled on time series points and a computational model that limits search space pruning. We built T-ReX to address this problem using two main building blocks: first, a MATCH_RECOGNIZE language extension that exposes the notion of segment variable and adds new operators, lending itself to better optimization; second, an executor capable of pruning the search space of matches and minimizing total query time using an optimizer. We conducted experiments using 5 real-world datasets and 11 query templates, including those from existing works. T-ReX outperformed an optimized NFA-based pattern search executor by 6x in median query time and an optimized tree-based executor by 19X.

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Index Terms

T-Rex: Optimizing Pattern Search on Time Series
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Online analytical processing engines

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Published In

cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Publication History

Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Cited By

Bergami G(2024)Streamlining Temporal Formal Verification over Columnar DatabasesInformation10.3390/info1501003415:1(34)Online publication date: 8-Jan-2024
https://doi.org/10.3390/info15010034
Bača R(2024)Window Function Expression: Let the Self-Join EnterProceedings of the VLDB Endowment10.14778/3665844.366584817:9(2162-2174)Online publication date: 6-Aug-2024
https://doi.org/10.14778/3665844.3665848
Yan YWong R(2024)Proximity Queries on Point Clouds using Rapid Construction Path OracleProceedings of the ACM on Management of Data10.1145/36392612:1(1-26)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639261
Liu SDai HSong SLi MDai JGu RChen GBaeza-Yates RBonchi F(2024)ACER: Accelerating Complex Event Recognition via Two-Phase Filtering under Range Bitmap-Based IndexesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671814(1933-1943)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671814
Bi YWu YLiu JRen KXiong L(2024)When Data Pricing Meets Non-Cooperative Game Theory2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00443(5548-5559)Online publication date: 13-May-2024
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Qiu TJiang SYang XWang BZong CZhu R(2024)An Efficient Algorithm for Continuous Complex Event Matching Using Bit-Parallelism2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00037(396-408)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00037

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