research-article

Dumpy: A Compact and Adaptive Index for Large Data Series Collections

Authors:

Themis Palpanas,

Wei WangAuthors Info & Claims

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

Article No.: 111, Pages 1 - 27

https://doi.org/10.1145/3588965

Published: 30 May 2023 Publication History

Abstract

Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and approximate similarity search, with approximate search providing high-quality results within milliseconds, which makes it very attractive for certain modern applications. Reducing the pre-processing (i.e., index building) time and improving the accuracy of search results are two major challenges. DSTree and the iSAX index family are state-of-the-art solutions for this problem. However, DSTree suffers from long index building times, while iSAX suffers from low search accuracy. In this paper, we identify two problems of the iSAX index family that adversely affect the overall performance. First, we observe the presence of a proximity-compactness trade-off related to the index structure design (i.e., the node fanout degree), significantly limiting the efficiency and accuracy of the resulting index. Second, a skewed data distribution will negatively affect the performance of iSAX. To overcome these problems, we propose Dumpy, an index that employs a novel multi-ary data structure with an adaptive node splitting algorithm and an efficient building workflow. Furthermore, we devise Dumpy-Fuzzy as a variant of Dumpy which further improves search accuracy by proper duplication of series. Experiments with a variety of large, real datasets demonstrate that the Dumpy solutions achieve considerably better efficiency, scalability and search accuracy than its competitors.

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Xiong HZhang HWang ZHe ZWang PWang X(2024)CIVET: Exploring Compact Index for Variable-Length Subsequence Matching on Time SeriesProceedings of the VLDB Endowment10.14778/3665844.366584517:9(2123-2135)Online publication date: 1-May-2024
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Index Terms

Dumpy: A Compact and Adaptive Index for Large Data Series Collections
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Temporal data
    2. Database management system engines
      1. Database query processing

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cover image Proceedings of the ACM on Management of Data

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

PACMMOD

May 2023

2807 pages

EISSN:2836-6573

DOI:10.1145/3603164

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Published: 30 May 2023

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Wei JPeng BLee XPalpanas T(2024)DET-LSH: A Locality-Sensitive Hashing Scheme with Dynamic Encoding Tree for Approximate Nearest Neighbor SearchProceedings of the VLDB Endowment10.14778/3665844.366585417:9(2241-2254)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665854
Hu HQiu JWang HLiang BZou S(2024)DIDS: Double Indices and Double Summarizations for Fast Similarity SearchProceedings of the VLDB Endowment10.14778/3665844.366585117:9(2198-2211)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665851
Xiong HZhang HWang ZHe ZWang PWang X(2024)CIVET: Exploring Compact Index for Variable-Length Subsequence Matching on Time SeriesProceedings of the VLDB Endowment10.14778/3665844.366584517:9(2123-2135)Online publication date: 1-May-2024
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Maroulis SStamatopoulos VPapastefanatos GTerrovitis M(2024)Visualization-Aware Time Series Min-Max Caching with Error Bound GuaranteesProceedings of the VLDB Endowment10.14778/3659437.365946017:8(2091-2103)Online publication date: 31-May-2024
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