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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
h4a0157f59405157e
Venue
SIGMOD
Year
2024
Pagerank
5.0979044e-05
Overall Rank
10,002 | 32.76%
DOI
10.1145/3677130

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gu_sigmod24,
        title = {{BT-Tree: A Reinforcement Learning Based Index for Big Trajectory Data}},
        author = {Gu, Tu and Feng, Kaiyu and Yang, Jingyi and Cong, Gao and Long, Cheng and Zhang, Rui},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3677130},
        url = {https://dl.acm.org/doi/10.1145/3677130},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,844 Rethinking Learned Index and LSM-tree Integration 2026 VLDB 4.9793485e-05
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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 0.001992968
4 The R*-tree: An Efficient and Robust Access Method for Points and Rectangles 1990 SIGMOD 0.0011405675
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
574 Indexing the Positions of Continuously Moving Objects 2000 SIGMOD 0.00016173068
868 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013354403
985 The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries 2003 VLDB 0.00012685169
1,132 Qd-tree: Learning Data Layouts for Big Data Analytics 2020 SIGMOD 0.00011898257
1,191 Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads 2021 VLDB 0.00011590153
1,226 Fractals for Secondary Key Retrieval 1989 PODS 0.00011431306
1,446 LISA: A Learned Index Structure for Spatial Data 2020 SIGMOD 0.00010629222
1,878 Effectively Learning Spatial Indices 2020 VLDB 9.4451309e-05
2,021 Distributed Trajectory Similarity Search 2017 VLDB 9.1719539e-05
2,196 DITA: Distributed In-Memory Trajectory Analytics 2018 SIGMOD 8.8780862e-05
2,310 The MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries 2001 VLDB 8.6626285e-05
2,421 Novel Approaches to the Indexing of Moving Object Trajectories 2000 VLDB 8.4897323e-05
2,911 Indexing Large Trajectory Data Sets With SETI* 2003 CIDR 7.8720638e-05
3,161 Effective Travel Time Estimation: When Historical Trajectories over Road Networks Matter 2020 SIGMOD 7.5796549e-05
4,134 ST2B-tree: A Self-Tunable Spatio-Temporal B+-tree Index for Moving Objects 2008 SIGMOD 6.787575e-05
4,850 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 6.3808017e-05
5,743 VRE: A Versatile, Robust, and Economical Trajectory Data System 2022 VLDB 6.0099017e-05
6,596 PPQ-Trajectory: Spatio-temporal Quantization for Querying in Large Trajectory Repositories 2021 VLDB 5.7410544e-05
7,303 Theoretically Optimal and Empirically Efficient R-trees with Strong Parallelizability 2018 VLDB 5.5598057e-05
8,179 Towards Designing and Learning Piecewise Space-Filling Curves 2023 VLDB 5.3826446e-05
9,427 Boosting Moving Object Indexing through Velocity Partitioning 2012 VLDB 5.178769e-05
10,168 PLATON: Top-down R-tree Packing with Learned Partition Policy 2023 SIGMOD 5.0682654e-05
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