Spatial data represent abstractions of real-world entities and can be obtained in various ways. They have properties that differentiate them from other types of data, such as a complex structure and dynamism. In recent years with the increasing volume of spatial data, referred to as “big geospatial data,” some tools have been developed to process such data efficiently, such as SpatialHadoop. The use of appropriate data indices based on the data set to be processed, as well as queries and operations to be performed, is essential for the optimal performance of these applications. In particular, since public cloud providers’ charges are based on the resources used, it is imperative to optimize application execution in order to avoid unnecessary expense. This article proposes the use of six conditions that seek to minimize the cost of processing big geospatial data in public cloud providers. The tests performed demonstrate that the use of these conditions and the choice of the lowest-cost provider can reduce the total processing cost by up to 41%.

中文翻译：

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在公共云提供商中最小化处理大地理空间数据的成本的建议

空间数据代表现实世界实体的抽象，可以通过多种方式获得。它们具有将它们与其他类型的数据区分开来的特性，例如复杂的结构和动态性。近年来，随着被称为“大地理空间数据”的空间数据量的不断增加，已经开发了一些工具来有效地处理此类数据，例如 SpatialHadoop。根据要处理的数据集使用适当的数据索引，以及要执行的查询和操作，对于这些应用程序的最佳性能至关重要。特别是，由于公有云提供商的收费是基于所使用的资源，因此优化应用程序执行以避免不必要的费用是势在必行的。本文提出了使用六个条件来寻求最小化公共云提供商处理大地理空间数据的成本。执行的测试表明，使用这些条件并选择成本最低的供应商可以将总处理成本降低多达 41%。