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Time Series Representation for Visualization in Apache IoTDB
Summary: Proposes M4-LSM, a chunk-merge-free M4 representation for time-series in LSM-based TSDBs, using chunk metadata and intra-chunk indexing to prune and avoid merges. Implemented in Apache IoTDB; real-data experiments show fast, precise M4 visualization with preserved accuracy.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6845
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.5098472e-05
- Overall Rank
- 8,426 | 41.44%
- DOI
-
10.1145/3639290
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 608 |
Monkey: Optimal Navigable Key-Value Store |
2017 |
SIGMOD |
0.00019233548 |
| 689 |
Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences |
1997 |
SIGMOD |
0.00018069077 |
| 1,309 |
Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging |
2018 |
SIGMOD |
0.00012655712 |
| 1,438 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011965956 |
| 1,804 |
M4: A Visualization-Oriented Time Series Data Aggregation |
2014 |
VLDB |
0.00010481427 |
| 2,112 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
9.5244583e-05 |
| 2,606 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
8.4621503e-05 |
| 3,321 |
Trajectory Simplification: An Experimental Study and Quality Analysis |
2018 |
VLDB |
7.2212455e-05 |
| 3,801 |
Plato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error Guarantees |
2020 |
VLDB |
6.7528979e-05 |
| 3,971 |
Apache IoTDB: A Time Series Database for IoT Applications |
2023 |
SIGMOD |
6.5733348e-05 |
| 5,070 |
Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB |
2022 |
VLDB |
5.7133478e-05 |
| 5,313 |
Key-Value Storage Engines |
2020 |
SIGMOD |
5.5711707e-05 |
| 9,048 |
On Repairing Timestamps for Regular Interval Time Series |
2022 |
VLDB |
4.3997447e-05 |
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| Overall Rank |
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Venue |
Pagerank |
| 10,584 |
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| 6,072 |
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| 9,797 |
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4.2777144e-05 |
| 1,925 |
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0.00010073156 |
| 11,052 |
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4.1905499e-05 |
| 10,391 |
In-Database Time Series Clustering |
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SIGMOD |
4.1905499e-05 |
| 5,070 |
Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB |
2022 |
VLDB |
5.7133478e-05 |
| 3,971 |
Apache IoTDB: A Time Series Database for IoT Applications |
2023 |
SIGMOD |
6.5733348e-05 |
| 1,804 |
M4: A Visualization-Oriented Time Series Data Aggregation |
2014 |
VLDB |
0.00010481427 |