AQWA: Adaptive Query-Workload-Aware Partitioning of Big Spatial Data
Summary: AQWA: adaptive, workload-aware partitioning that incrementally updates partitions as data arrives and queries execute, with no distribution/workload. Twitter data with range/kNN workloads shows AQWA delivering up to 10x faster queries than static partitioning. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ahmed M. Aly
- 2. Ahmed R. Mahmood
- 3. Mohamed S. Hassan
- 4. Walid G. Aref
- 5. Mourad Ouzzani
- 6. Hazem Elmeleegy
- 7. Thamir Qadah
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,646 | LocationSpark: A Distributed In-Memory Data Management System for Big Spatial Data | 2016 | VLDB | 6.0176549e-05 |
| 7,268 | Comparing Synopsis Techniques for Approximate Spatial Data Analysis | 2019 | VLDB | 4.7769096e-05 |
| 7,925 | Architecting a Query Compiler for Spatial Workloads | 2020 | SIGMOD | 4.610914e-05 |
| 8,704 | Incremental Partitioning for Efficient Spatial Data Analytics | 2022 | VLDB | 4.4596039e-05 |
| 9,769 | Adaptive Indexing of Objects with Spatial Extent | 2023 | VLDB | 4.2815042e-05 |
| 9,804 | Amoeba: A Shape changing Storage System for Big Data | 2016 | VLDB | 4.2774538e-05 |
| 9,952 | Distributed Stream KNN Join | 2021 | SIGMOD | 4.236537e-05 |
| 11,606 | SSTD: A Distributed System on Streaming Spatio-Textual Data | 2020 | VLDB | 4.1905499e-05 |
| 11,782 | Query Processing Techniques for Big Spatial-Keyword Data | 2017 | SIGMOD | 4.1905499e-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.
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