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BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data
Summary: BT-Tree: a reinforcement-learning–driven index for big trajectory data, built by recursive bi-partitioning to accelerate range and KNN queries. It combines a cost-function-based build (CFBM) that encodes data and workload, with an RL refinement to avoid locally optimal cuts; experiments with up to 800M points show CFBM outperforms baselines, and RL consistently wins, especially on large datasets.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6961
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,983 | 23.67%
- DOI
-
10.1145/3677130
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 2 |
R-Trees: A Dynamic Index Structure For Spatial Searching |
1984 |
SIGMOD |
0.0032118946 |
| 6 |
The R*-tree: An Efficient and Robust Access Method for Points and Rectangles |
1990 |
SIGMOD |
0.0016113151 |
| 101 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049778866 |
| 628 |
Indexing the Positions of Continuously Moving Objects |
2000 |
SIGMOD |
0.00018957327 |
| 1,055 |
The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries |
2003 |
VLDB |
0.00014391376 |
| 1,250 |
Fractals for Secondary Key Retrieval |
1989 |
PODS |
0.00013037936 |
| 1,464 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.0001184772 |
| 1,608 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011169837 |
| 1,749 |
Distributed Trajectory Similarity Search |
2017 |
VLDB |
0.00010684148 |
| 1,887 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010201938 |
| 2,108 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5283642e-05 |
| 2,198 |
DITA: Distributed In-Memory Trajectory Analytics |
2018 |
SIGMOD |
9.3096071e-05 |
| 2,501 |
The MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries |
2001 |
VLDB |
8.6367789e-05 |
| 2,558 |
Novel Approaches to the Indexing of Moving Object Trajectories |
2000 |
VLDB |
8.5441554e-05 |
| 2,676 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.326321e-05 |
| 2,744 |
Indexing Large Trajectory Data Sets With SETI* |
2003 |
CIDR |
8.1917832e-05 |
| 3,453 |
Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter |
2020 |
SIGMOD |
7.0785866e-05 |
| 4,585 |
ST2B-tree: A Self-Tunable Spatio-Temporal B+-tree Index for Moving Objects |
2008 |
SIGMOD |
6.0596467e-05 |
| 5,463 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
5.4920768e-05 |
| 6,183 |
PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories |
2021 |
VLDB |
5.1636638e-05 |
| 7,022 |
VRE: A Versatile, Robust, and Economical Trajectory Data System |
2022 |
VLDB |
4.8534529e-05 |
| 7,046 |
Theoretically Optimal and Empirically Efficient R-trees with Strong Parallelizability |
2018 |
VLDB |
4.8471584e-05 |
| 8,089 |
Towards Designing and Learning Piecewise Space-Filling Curves |
2023 |
VLDB |
4.5855593e-05 |
| 8,591 |
Boosting Moving Object Indexing through Velocity Partitioning |
2012 |
VLDB |
4.4851366e-05 |
| 9,826 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2710095e-05 |
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