Demonstrating Waffle: A Self-driving Grid Index
Summary: Waffle: a self-driving grid index for moving objects that automates grid partitioning, indexing and operational tuning to handle spatio-temporal mobility. Demonstrates architecture, workflow and user scenarios showing hands-off management and continuous performance improvement via automated adaptation. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Dalsu Choi (Korea University)
- 2. Hyunsik Yoon (Korea University)
- 3. Hyubjin Lee (Korea University)
- 4. Yon Dohn Chung (Korea University)
BibTeX Citation
@article{choi_vldb23,
title = {{Demonstrating Waffle: A Self-driving Grid Index}},
author = {Choi, Dalsu and Yoon, Hyunsik and Lee, Hyubjin and Chung, Yon Dohn},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {3954--3957},
doi = {10.14778/3611540.3611594},
url = {https://doi.org/10.14778/3611540.3611594},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 86 | Automatic Database Management System Tuning Through Large-scale Machine Learning | 2017 | SIGMOD | 0.00035316107 |
| 334 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00020875082 |
| 498 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00017440583 |
| 1,344 | An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems | 2021 | VLDB | 0.00011094717 |
| 1,840 | Effectively Learning Spatial Indices | 2020 | VLDB | 9.6404567e-05 |
| 3,400 | A Demonstration of the OtterTune Automatic Database Management System Tuning Service | 2018 | VLDB | 7.4433294e-05 |
| 9,739 | Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System | 2022 | VLDB | 5.227679e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 6,488 | Parallel Main-Memory Indexing for Moving-Object Query and Update Workloads | 2012 | SIGMOD |
| 2 | 10,718 | Demonstrating MAST: An Efficient System for Point Cloud Data Analytics | 2025 | SIGMOD |
| 3 | 3,394 | Prediction and Indexing of Moving Objects with Unknown Motion Patterns | 2004 | SIGMOD |
| 4 | 1,698 | A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects | 2005 | SIGMOD |
| 5 | 7,463 | Effectively Indexing Uncertain Moving Objects for Predictive Queries | 2009 | VLDB |
| 6 | 13,674 | Manage and Query Generic Moving Objects in SECONDO | 2012 | VLDB |
| 7 | 8,434 | Waffle: An Online Oblivious Datastore for Protecting Data Access Patterns | 2023 | SIGMOD |
| 8 | 560 | Indexing the Positions of Continuously Moving Objects | 2000 | SIGMOD |
| 9 | 8,200 | Waffle: A Workload-Aware and Query-Sensitive Framework for Disk-Based Spatial Indexing | 2023 | VLDB |
| 10 | 9,739 | Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System | 2022 | VLDB |