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首页> 外文期刊>International journal of information system modeling and design >Modeling and Computing Overlapping Aggregation of Large Data Sequences in Geographic Information Systems
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Modeling and Computing Overlapping Aggregation of Large Data Sequences in Geographic Information Systems

机译：地理信息系统中大数据序列的重叠聚合建模和计算

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摘要

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.
机译：最近，在信息系统领域，地理参考数据的获取在技术方面取得了巨大的飞跃。在数据处理优化方面存在一个实际问题，并且已经提出了不同的研究工作来基于多核方法来分析大型地理参考数据集。在本文中，对基于图形处理单元（GPGPU）通用逻辑的不同方法进行了建模和比较，以并行化栅格序列的重叠聚合。我们的方法在一系列代表同一区域温度随时间变化的栅格上进行了测试。每个栅格对应一个不同的数据采集时间段，并且每个栅格地理参考像元都与一个温度值关联。本文提出了一种优化方法，可以为确定长度的所有可能的栅格子序列计算区域的平均温度，即计算重叠的聚合数据摘要。在这些聚合中，相同的值子集会聚合几次。例如，这种类型的聚集可用于不同的环境数据分析中，例如，以预先计算数据库中的所有平均温度。本文重点介绍了性能的显着提高，并表明使用GPGPU并行处理使我们能够以比包括数据传输成本的顺序方法快50倍以上的速度运行聚合，而在没有数据传输成本的情况下，运行速度快200倍以上。

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