Abstract
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.
Highlights
With the emergence and the production of a large volume of spatial data, supporting large-scale and high-performance queries becomes crucial and essential in several fields.The tremendous advances in technology such as smartphones, the internet of things, web, navigation systems and sensors, have led to the production of large size spatial datasets.For instance, data related to climate and precision agriculture sectors is produced in high precision and large temporal sequences [1,2].Processing this large volume of data is both a challenge and a real opportunity
Driven by the challenges related to large-scale raster data processing and motivated by the power provided by the GPGPUs, we propose and test, in this paper, a GPGPUbased method to implement the following traditional raster query: the selection of spatio
The results presented in the paper show that GPGPU-based methods reduce the execution time and enable us to obtain the query response three times faster than the sequential methods
Summary
With the emergence and the production of a large volume of spatial data, supporting large-scale and high-performance queries becomes crucial and essential in several fields.The tremendous advances in technology such as smartphones, the internet of things, web, navigation systems and sensors, have led to the production of large size spatial datasets.For instance, data related to climate and precision agriculture sectors is produced in high precision and large temporal sequences [1,2].Processing this large volume of data is both a challenge and a real opportunity. With the emergence and the production of a large volume of spatial data, supporting large-scale and high-performance queries becomes crucial and essential in several fields. Data related to climate and precision agriculture sectors is produced in high precision and large temporal sequences [1,2]. Processing this large volume of data is both a challenge and a real opportunity. Querying large-scale data allows for extracting more valuable and meaningful information that is vital for decision making, scientific advancement and scenario predictions. Spatial query processing must be fast and able to handle more large spatial data efficiently which is the goal of our work using raster data.
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