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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
- 6844
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.5141748e-05
- Overall Rank
- 8,434 | 41.33%
- 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 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 609 |
Monkey: Optimal Navigable Key-Value Store |
2017 |
SIGMOD |
0.0001923446 |
| 693 |
Efficiently Supporting Ad Hoc Queries in Large Datasets of Time Sequences |
1997 |
SIGMOD |
0.00018077335 |
| 1,311 |
Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging |
2018 |
SIGMOD |
0.00012657439 |
| 1,460 |
Benchmarking Learned Indexes |
2021 |
VLDB |
0.00011887068 |
| 1,805 |
M4: A Visualization-Oriented Time Series Data Aggregation |
2014 |
VLDB |
0.00010493299 |
| 2,109 |
The Log-Structured Merge-Bush & the Wacky Continuum |
2019 |
SIGMOD |
9.5318694e-05 |
| 2,606 |
Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn |
2019 |
CIDR |
8.4645832e-05 |
| 3,318 |
Trajectory Simplification: An Experimental Study and Quality Analysis |
2018 |
VLDB |
7.2282052e-05 |
| 3,798 |
Plato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error Guarantees |
2020 |
VLDB |
6.7592302e-05 |
| 3,967 |
Apache IoTDB: A Time Series Database for IoT Applications |
2023 |
SIGMOD |
6.5796647e-05 |
| 5,071 |
Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB |
2022 |
VLDB |
5.7188461e-05 |
| 5,308 |
Key-Value Storage Engines |
2020 |
SIGMOD |
5.576303e-05 |
| 9,048 |
On Repairing Timestamps for Regular Interval Time Series |
2022 |
VLDB |
4.4039656e-05 |
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SIGMOD |
4.1945683e-05 |
| 5,071 |
Time Series Data Encoding for Efficient Storage: A Comparative Analysis in Apache IoTDB |
2022 |
VLDB |
5.7188461e-05 |
| 3,967 |
Apache IoTDB: A Time Series Database for IoT Applications |
2023 |
SIGMOD |
6.5796647e-05 |
| 1,805 |
M4: A Visualization-Oriented Time Series Data Aggregation |
2014 |
VLDB |
0.00010493299 |