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A demonstration of AQWA: adaptive query-workload-aware partitioning of big spatial data by Ahmed M. Aly; Ahmed S. Abdelhamid; Ahmed R. Mahmood; Walid G. Aref; Mohamed S. Hassan; Hazem Elmeleegy; Mourad Ouzzani is a Computer Science article available to read on EtoBox.
What is A demonstration of AQWA: adaptive query-workload-aware partitioning of big spatial data about?
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 ( __k__ NN, for short) queries over large-scale spatial datasets, and we exploit the performance of AQWA under different query-workloads.
Who reads A demonstration of AQWA: adaptive query-workload-aware partitioning of big spatial data?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Ahmed M. Aly; Ahmed S. Abdelhamid; Ahmed R. Mahmood; Walid G. Aref; Mohamed S. Hassan; Hazem Elmeleegy; Mourad Ouzzani
- Publisher
- ACM
- Published
- 2015
- Language
- EN
- Field
- Computer Science (Physical Sciences)