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CIVET: Exploring Compact Index for Variable-Length Subsequence Matching on Time Series
Summary: CIVET introduces UPAA, a uniform PAA representation that aligns features across variable-length subsequences while preserving lower-bounding. Builds a compact index by grouping adjacent/similar subsequences and uses pruning+filtering to enable exact (no false dismissals), scalable, faster variable-length matching.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13446
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.1905499e-05
- Overall Rank
- 11,025 | 23.38%
- DOI
-
10.14778/3665844.3665845
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Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 16 of 16 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 65 |
Fast Subsequence Matching in Time-Series Databases |
1994 |
SIGMOD |
0.00061977022 |
| 537 |
Fast Time Sequence Indexing for Arbitrary L_p Norms |
2000 |
VLDB |
0.00020650291 |
| 1,060 |
Warping Indexes with Envelope Transforms for Query by Humming |
2003 |
SIGMOD |
0.00014359912 |
| 1,157 |
A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series |
2013 |
VLDB |
0.00013600695 |
| 1,164 |
Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures |
2008 |
VLDB |
0.00013572951 |
| 3,033 |
A Decade of Progress in Indexing and Mining Large Time Series Databases |
2006 |
VLDB |
7.6737668e-05 |
| 3,544 |
Scalable, Variable-Length Similarity Search in Data Series: The ULISSE Approach |
2018 |
VLDB |
6.98759e-05 |
| 3,728 |
Indexing Large Human-Motion Databases |
2004 |
VLDB |
6.8082657e-05 |
| 4,217 |
Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series |
2018 |
SIGMOD |
6.3439684e-05 |
| 4,854 |
Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures |
2020 |
SIGMOD |
5.8707943e-05 |
| 5,156 |
Coconut: A Scalable Bottom-Up Approach for Building Data Series Indexes |
2018 |
VLDB |
5.6534878e-05 |
| 5,777 |
ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines |
2020 |
SIGMOD |
5.3277177e-05 |
| 7,090 |
Dumpy: A Compact and Adaptive Index for Large Data Series Collections |
2023 |
SIGMOD |
4.8318862e-05 |
| 8,775 |
The Inherent Time Complexity and An Efficient Algorithm for Subsequence Matching Problem |
2022 |
VLDB |
4.4500701e-05 |
| 9,431 |
Hum-a-song: A Subsequence Matching with Gaps-Range-Tolerances Query-By-Humming System |
2012 |
VLDB |
4.3399748e-05 |
| 9,434 |
A Subsequence Matching with Gaps-Range-Tolerances Framework: A Query-By-Humming Application |
2011 |
VLDB |
4.3399748e-05 |
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2022 |
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4.4500701e-05 |
| 65 |
Fast Subsequence Matching in Time-Series Databases |
1994 |
SIGMOD |
0.00061977022 |