Modeling and Computing Overlapping Aggregation of Large Data Sequences in Geographic Information Systems

Driss En-Nejjary, Francois Pinet and Myoung-Ah Kang
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Driss En-Nejjary: Université Clermont Auvergne, LIMOS, Irstea, UR TSCF, Clermont-Ferrand, France
Francois Pinet: Université Clermont Auvergne, Irstea, UR TSCF, Centre de Clermont-Ferrand, France
Myoung-Ah Kang: Université Clermont-Auvergne, LIMOS, Clermont-Ferrand, France

International Journal of Information System Modeling and Design (IJISMD), 2019, vol. 10, issue 1, 20-41

Abstract: Recently, in the field of information systems, the acquisition of geo-referenced data has made a huge leap forward in terms of technology. There is a real issue in terms of the data processing optimization, and different research works have been proposed to analyze large geo-referenced datasets based on multi-core approaches. In this article, different methods based on general-purpose logic on graphics processing unit (GPGPU) are modelled and compared to parallelize overlapping aggregations of raster sequences. Our methods are tested on a sequence of rasters representing the evolution of temperature over time for the same region. Each raster corresponds to a different data acquisition time period, and each raster geo-referenced cell is associated with a temperature value. This article proposes optimized methods to calculate the average temperature for the region for all the possible raster subsequences of a determined length, i.e., to calculate overlapping aggregated data summaries. In these aggregations, the same subsets of values are aggregated several times. For example, this type of aggregation can be useful in different environmental data analyses, e.g., to pre-calculate all the average temperatures in a database. The present article highlights a significant increase in performance and shows that the use of GPGPU parallel processing enabled us to run the aggregations up to more than 50 times faster than the sequential method including data transfer cost and more than 200 times faster without data transfer cost.

Date: 2019
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