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Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System

Summary: Waffle is an in-memory grid index for moving objects that groups neighboring cells into tunable chunks to balance heavy updates and scan queries. WaffleMaker uses online reinforcement learning to tune index knobs and rebuild non-blockingly as movement workloads evolve. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
he7226cc5103ec305
Venue
VLDB
Year
2022
Pagerank
5.1079647e-05
Overall Rank
9,926 | 33.29%
DOI
10.14778/3551793.3551800
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{choi_vldb22,
        title = {{Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System}},
        author = {Choi, Dalsu and Yoon, Hyunsik and Lee, Hyubjin and Chung, Yon Dohn},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2375--2388},
        doi = {10.14778/3551793.3551800},
        url = {https://doi.org/10.14778/3551793.3551800},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,667 LM-Tree: A Hybrid Learned Index for Similarity Search in Metric Spaces 2026 SIGMOD 4.9769913e-05
11,794 Demonstrating Waffle: A Self-driving Grid Index 2023 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046363107
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036675568
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021276452
322 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021034201
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018310278
869 Learning Multi-dimensional Indexes 2020 SIGMOD 0.00013363241
1,188 Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads 2021 VLDB 0.00011598149
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,289 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011159167
1,440 LISA: A Learned Index Structure for Spatial Data 2020 SIGMOD 0.00010638444
1,877 Effectively Learning Spatial Indices 2020 VLDB 9.4498401e-05
2,721 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 8.0931221e-05
3,032 iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases 2019 VLDB 7.7317595e-05
3,159 CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions 2021 VLDB 7.5797912e-05
4,851 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 6.377837e-05
6,618 Parallel Main-Memory Indexing for Moving-Object Query and Update Workloads 2012 SIGMOD 5.7313827e-05
8,264 The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual” 2021 VLDB 5.3648071e-05
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