Performance optimization of grid aggregation in spatial data warehouses

Kang, Myoung-Ah; Zaamoune, Mehdi; Pinet, François; Bimonte, Sandro; Beaune, Philippe

doi:10.1080/17538947.2014.962999

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…Raster data (also known as grid data) is a georeferenced 2-D matrix of cells [5]. Each cell in this matrix has two coordinates (x, y) and is associated with a measurement value which can be temperature, pressure, humidity, CO2 emissions, etc.…”

Section: Background 221 Spatio-temporal Raster Datamentioning

confidence: 99%

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Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

En-Nejjary

Pinet

Kang

2021

IJGI

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The size of spatial data is growing intensively due to the emergence of and the tremendous advances in technology such as sensors and the internet of things. Supporting high-performance queries on this large volume of data becomes essential in several data- and compute-intensive applications. Unfortunately, most of the existing methods and approaches are based on a traditional computing framework (uniprocessors) which makes them not scalable and not adequate to deal with large-scale data. In this work, we present a high-performance query for massive spatio–temporal data. The query consists of selecting fixed size raster subsequences, based on the average of their region of interest, from a spatio–temporal raster sequence satisfying a user threshold condition. In our paper, for the purpose of simplification, we consider that the region of interest is the entire raster and not only a subregion. Our aim is to speed up the execution using parallel primitives and pure CUDA. Furthermore, we propose a new method based on a sorting step to save computations and boost the speed of the query execution. The test results show that the proposed methods are faster and good performance is achieved even with large-scale rasters and data.

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Section: Background 221 Spatio-temporal Raster Datamentioning

confidence: 99%

“…Each cell in this matrix has (x, y) coordinates and value measurements which can be, for instance, a temperature, air pressure, humidity, CO2 emissions, etc. [5]. Map algebra is a set of conventions and techniques for raster processing [6].…”

Section: Introductionmentioning

confidence: 99%

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

En-Nejjary

Pinet

Kang

2021

IJGI

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“…Several of these large data sources are represented in the form of rasters, e.g., georeferenced regular grids [12][13]. This type of 2-dimmensional grids constitutes a traditional geographical data format.…”

Section: Introductionmentioning

confidence: 99%

A Method to Improve the Performance of Raster Selection Based on a User-Defined Condition: An Example of Application for Agri-environmental Data

En-Nejjary¹,

Pinet²,

Kang³

2018

Advances in Intelligent Systems and Computing

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More and more environmental and agricultural data are now acquired with a high precision and temporal frequency. These data are often represented in the form of rasters and are useful for agricultural activities or climate change analyses. In this paper, we propose a new method to process very large raster. We present a new technique to improve the execution time of the selection and calculation of data summaries (e.g., the average temperature for a region) on a temporal sequence of rasters. We illustrate the use of our approach on the case of temperature data, which is important information both for agriculture and for climate change analyses. We have generated several data sets in order to analyze the influence of the different value properties on the process performance. One of our final goals is to provide information about the value conditions in which the proposed processing should be used.

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

En-Nejjary

Pinet

Kang

2019

International Journal of Information System Modeling and Design

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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.

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Performance optimization of grid aggregation in spatial data warehouses

Cited by 3 publications

References 18 publications

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

Spatial Data Sequence Selection Based on a User-Defined Condition Using GPGPU

A Method to Improve the Performance of Raster Selection Based on a User-Defined Condition: An Example of Application for Agri-environmental Data

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

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