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Analyzing spatial analytics systems based on Hadoop and Spark: A user perspective
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-08-31 , DOI: 10.1002/spe.2882
João Pedro Castro 1, 2 , Anderson Carniel 3 , Cristina Ciferri 1
Affiliation  

Spatial analytics systems (SASs) represent a technology capable of managing huge volumes of spatial data using frameworks such as Apache Hadoop and Apache Spark. An increasing number of SASs have been proposed, requiring a comparison among them. However, existing comparisons in the literature provide a system‐centric view based on performance evaluations. Thus, there is a lack of comparisons based on the user‐centric view, that is, comparisons that help users to understand how the characteristics of SASs are useful to meet the specific requirements of their spatial applications. In this article, we provide a user‐centric comparison of the following SASs based on Hadoop and Spark: Hadoop‐GIS, SpatialHadoop, SpatialSpark, GeoSpark, GeoMesa Spark, SIMBA, LocationSpark, STARK, Magellan, SparkGIS, and Elcano. This comparison employs an extensive set of criteria related to the general characteristics of these systems, to the aspects of spatial data handling, and to the aspects inherent to distributed systems. Based on this comparison, we introduce guidelines to help users to choose an appropriate SAS. We also describe two case studies based on real‐world applications to illustrate the use of these guidelines. Finally, we discuss chronological tendencies related to SASs and identify limitations that SASs should address to improve user experience.

中文翻译:

分析基于 Hadoop 和 Spark 的空间分析系统:用户视角

空间分析系统 (SAS) 代表一种能够使用 Apache Hadoop 和 Apache Spark 等框架管理大量空间数据的技术。越来越多的 SAS 被提出,需要在它们之间进行比较。然而,文献中现有的比较提供了基于绩效评估的以系统为中心的观点。因此,缺乏基于以用户为中心的观点的比较,即帮助用户了解 SAS 的特性如何有助于满足其空间应用的特定要求的比较。在本文中,我们对以下基于 Hadoop 和 Spark 的 SAS 进行了以用户为中心的比较:Hadoop-GIS、SpatialHadoop、SpatialSpark、GeoSpark、GeoMesa Spark、SIMBA、LocationSpark、STARK、Magellan、SparkGIS 和 Elcano。这种比较采用了一套广泛的标准,这些标准与这些系统的一般特征、空间数据处理的方面以及分布式系统的固有方面有关。基于这种比较,我们引入了指导方针来帮助用户选择合适的 SAS。我们还描述了两个基于实际应用的案例研究,以说明这些指南的使用。最后,我们讨论了与 SAS 相关的按时间顺序排列的趋势,并确定了 SAS 应该解决的限制以改善用户体验。我们还描述了两个基于实际应用的案例研究,以说明这些指南的使用。最后,我们讨论了与 SAS 相关的按时间顺序排列的趋势,并确定了 SAS 应该解决的限制以改善用户体验。我们还描述了两个基于实际应用的案例研究,以说明这些指南的使用。最后,我们讨论了与 SAS 相关的按时间顺序排列的趋势,并确定了 SAS 应该解决的限制以改善用户体验。
更新日期:2020-08-31
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