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
Edition12
DOIs
Publication statusPublished - 1 Jan 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

Publication series

NameProceedings of the VLDB Endowment
Number12
Volume8
ISSN (Electronic)2150-8097

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

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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., pp. 1968-1971). (Proceedings of the VLDB Endowment; Vol. 8, No. 12). Association for Computing Machinery. https://doi.org/10.14778/2824032.2824113