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A Demonstration of AQWA: Adaptive Query-Workload-Aware Partitioning of Big Spatial Data

Summary: AQWA provides adaptive, query-workload- and data-aware partitioning for distributed spatial data, incrementally refining layouts without prior workload/distribution knowledge. Demonstrated on Hadoop and Spark for range and kNN queries. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11282
Venue
VLDB
Year
2015
Pagerank
5.093636e-05
Overall Rank
12,141 | 16.71%
DOI
10.14778/2824032.2824113

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Authors

BibTeX Citation

@article{aly_vldb15,
        title = {{A Demonstration of AQWA: Adaptive Query-Workload-Aware Partitioning of Big Spatial Data}},
        author = {Aly, Ahmed M. and Abdelhamid, Ahmed S. and Mahmood, Ahmed R. and Aref, Walid G. and Hassan, Mohamed S. and Elmeleegy, Hazem and Ouzzani, Mourad},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {12},
        pages = {1968},
        doi = {10.14778/2824032.2824113},
        url = {https://doi.org/10.14778/2824032.2824113},
        year = {2015}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
8,568 Prompt: Dynamic Data-Partitioning for Distributed Micro-batch Stream Processing Systems 2020 SIGMOD 5.4118703e-05
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