A demonstration of AQWA: Adaptive query-workload aware partitioning of big spatial data

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

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

The ubiquity of location-aware devices, e.g., smartphones and GPS devices, has led to a plethora of location-based services in which huge amounts of geotagged information need to be efficiently processed by large-scale computing clusters. This demo presents AQWA, an adaptive and query-workload-aware data partitioning mechanism for processing large-scale spatial data. Unlike existing cluster-based systems, e.g., SpatialHadoop, that apply static partitioning of spatial data, AQWA has the ability to react to changes in the query-workload and data distribution. A key feature of AQWA is that it does not assume prior knowledge of the query-workload or data distribution. Instead, AQWA reacts to changes in both the data and the query-workload by incrementally updating the partitioning of the data. We demonstrate two prototypes of AQWA deployed over Hadoop and Spark. In both prototypes, we process spatial range and k-nearest-neighbor (kNN, for short) queries over largescale spatial datasets, and we exploit the performance of AQWA under different query-workloads.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages1968-1971
Number of pages4
Volume8
Edition12
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
Smartphones
Electric sparks
Global positioning system
Demonstrations
Processing

ASJC Scopus subject areas

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

Cite this

Aly, A. M., Abdelhamid, A. S., Mahmood, A. R., Aref, W. G., Hassan, M. S., Elmeleegy, H., & Ouzzani, M. (2015). A demonstration of AQWA: Adaptive query-workload aware partitioning of big spatial data. In Proceedings of the VLDB Endowment (12 ed., Vol. 8, pp. 1968-1971). Association for Computing Machinery.

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

Proceedings of the VLDB Endowment. Vol. 8 12. ed. Association for Computing Machinery, 2015. p. 1968-1971.

Research output: Chapter in Book/Report/Conference proceedingChapter

Aly, AM, Abdelhamid, AS, Mahmood, AR, Aref, WG, Hassan, MS, Elmeleegy, H & Ouzzani, M 2015, A demonstration of AQWA: Adaptive query-workload aware partitioning of big spatial data. in Proceedings of the VLDB Endowment. 12 edn, vol. 8, Association for Computing Machinery, pp. 1968-1971, 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, Abdelhamid AS, Mahmood AR, Aref WG, Hassan MS, Elmeleegy H et al. A demonstration of AQWA: Adaptive query-workload aware partitioning of big spatial data. In Proceedings of the VLDB Endowment. 12 ed. Vol. 8. Association for Computing Machinery. 2015. p. 1968-1971
Aly, Ahmed M. ; Abdelhamid, Ahmed S. ; Mahmood, Ahmed R. ; Aref, Walid G. ; Hassan, Mohamed S. ; Elmeleegy, Hazem ; Ouzzani, Mourad. / A demonstration of AQWA : Adaptive query-workload aware partitioning of big spatial data. Proceedings of the VLDB Endowment. Vol. 8 12. ed. Association for Computing Machinery, 2015. pp. 1968-1971
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