AQWA

Adaptive query-workload-aware partitioning of big spatial data

Ahmed M. Aly, Ahmed R. Mahmood, Mohamed S. Hassan, Walid G. Aref, Mourad Ouzzani, Hazem Elmeleegy, Thamir Qadah

Research output: Chapter in Book/Report/Conference proceedingChapter

20 Citations (Scopus)

Abstract

The unprecedented spread of location-aware devices has resulted in a plethora of location-based services in which huge amounts of spa- tial data need to be efficiently processed by large-scale computing clusters. Existing cluster-based systems for processing spatial data employ static data-partitioning structures that cannot adapt to data changes, and that are insensitive to the query workload. Hence, these systems are incapable of consistently providing good per- formance. To close this gap, we present AQWA, an adaptive and query-workload-aware mechanism for partitioning large-scale spa- tial data. AQWA does not assume prior knowledge of the data dis- tribution or the query workload. Instead, as data is consumed and queries are processed, the data partitions are incrementally updated. With extensive experiments using real spatial data from Twitter, and various workloads of range and k-nearest-neighbor queries, we demonstrate that AQWA can achieve an order of magnitude en- hancement in query performance compared to the state-of-the-art systems.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages2062-2073
Number of pages12
Volume8
Edition13
Publication statusPublished - 2015
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: 11 Sep 200611 Sep 2006

Other

Other3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
CountryKorea, Republic of
CitySeoul
Period11/9/0611/9/06

Fingerprint

Cluster computing
Location based services
Processing
Experiments

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Aly, A. M., Mahmood, A. R., Hassan, M. S., Aref, W. G., Ouzzani, M., Elmeleegy, H., & Qadah, T. (2015). AQWA: Adaptive query-workload-aware partitioning of big spatial data. In Proceedings of the VLDB Endowment (13 ed., Vol. 8, pp. 2062-2073). Association for Computing Machinery.

AQWA : Adaptive query-workload-aware partitioning of big spatial data. / Aly, Ahmed M.; Mahmood, Ahmed R.; Hassan, Mohamed S.; Aref, Walid G.; Ouzzani, Mourad; Elmeleegy, Hazem; Qadah, Thamir.

Proceedings of the VLDB Endowment. Vol. 8 13. ed. Association for Computing Machinery, 2015. p. 2062-2073.

Research output: Chapter in Book/Report/Conference proceedingChapter

Aly, AM, Mahmood, AR, Hassan, MS, Aref, WG, Ouzzani, M, Elmeleegy, H & Qadah, T 2015, AQWA: Adaptive query-workload-aware partitioning of big spatial data. in Proceedings of the VLDB Endowment. 13 edn, vol. 8, Association for Computing Machinery, pp. 2062-2073, 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006, Seoul, Korea, Republic of, 11/9/06.
Aly AM, Mahmood AR, Hassan MS, Aref WG, Ouzzani M, Elmeleegy H et al. AQWA: Adaptive query-workload-aware partitioning of big spatial data. In Proceedings of the VLDB Endowment. 13 ed. Vol. 8. Association for Computing Machinery. 2015. p. 2062-2073
Aly, Ahmed M. ; Mahmood, Ahmed R. ; Hassan, Mohamed S. ; Aref, Walid G. ; Ouzzani, Mourad ; Elmeleegy, Hazem ; Qadah, Thamir. / AQWA : Adaptive query-workload-aware partitioning of big spatial data. Proceedings of the VLDB Endowment. Vol. 8 13. ed. Association for Computing Machinery, 2015. pp. 2062-2073
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