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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
- 6960
- Venue
- SIGMOD
- Year
- 2024
- Pagerank
- 4.1945683e-05
- Overall Rank
- 10,980 | 23.62%
- 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 |
0.0032169493 |
| 6 |
The R*-tree: An Efficient and Robust Access Method for Points and Rectangles |
1990 |
SIGMOD |
0.0016162015 |
| 102 |
The Case for Learned Index Structures |
2018 |
SIGMOD |
0.00049545203 |
| 631 |
Indexing the Positions of Continuously Moving Objects |
2000 |
SIGMOD |
0.00018935493 |
| 1,067 |
The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries |
2003 |
VLDB |
0.00014327945 |
| 1,249 |
Fractals for Secondary Key Retrieval |
1989 |
PODS |
0.00013044758 |
| 1,478 |
Learning Multi-dimensional Indexes |
2020 |
SIGMOD |
0.00011762542 |
| 1,611 |
Qd-tree: Learning Data Layouts for Big Data Analytics |
2020 |
SIGMOD |
0.00011147324 |
| 1,776 |
Distributed Trajectory Similarity Search |
2017 |
VLDB |
0.00010593716 |
| 1,889 |
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads |
2021 |
VLDB |
0.00010200865 |
| 2,115 |
LISA: A Learned Index Structure for Spatial Data |
2020 |
SIGMOD |
9.5257379e-05 |
| 2,192 |
DITA: Distributed In-Memory Trajectory Analytics |
2018 |
SIGMOD |
9.3185895e-05 |
| 2,499 |
The MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries |
2001 |
VLDB |
8.646204e-05 |
| 2,541 |
Novel Approaches to the Indexing of Moving Object Trajectories |
2000 |
VLDB |
8.5795657e-05 |
| 2,678 |
Effectively Learning Spatial Indices |
2020 |
VLDB |
8.3252088e-05 |
| 2,738 |
Indexing Large Trajectory Data Sets With SETI* |
2003 |
CIDR |
8.2005452e-05 |
| 3,447 |
Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter |
2020 |
SIGMOD |
7.0854131e-05 |
| 4,683 |
ST2B-tree: A Self-Tunable Spatio-Temporal B+-tree Index for Moving Objects |
2008 |
SIGMOD |
6.0010961e-05 |
| 5,572 |
The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data |
2023 |
SIGMOD |
5.4277273e-05 |
| 6,181 |
PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories |
2021 |
VLDB |
5.1686247e-05 |
| 7,021 |
VRE: A Versatile, Robust, and Economical Trajectory Data System |
2022 |
VLDB |
4.8581131e-05 |
| 7,054 |
Theoretically Optimal and Empirically Efficient R-trees with Strong Parallelizability |
2018 |
VLDB |
4.8496866e-05 |
| 8,405 |
Towards Designing and Learning Piecewise Space-Filling Curves |
2023 |
VLDB |
4.5224126e-05 |
| 8,592 |
Boosting Moving Object Indexing through Velocity Partitioning |
2012 |
VLDB |
4.4894309e-05 |
| 9,827 |
PLATON: Top-down R-tree Packing with Learned Partition Policy |
2023 |
SIGMOD |
4.2751057e-05 |
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