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A framework for co-location patterns mining in big spatial data

机译：大空间数据中共置模式挖掘的框架

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Modern big data platforms such as Apache Hadoop and Apache Spark are able to process and analyse huge data sets, but still lack comprehensive support for spatial data analysis. Nevertheless, spatial data mining requires an efficient distributed processing of big spatial data. Spatial data mining is a subclass of data mining, which mainly focuses on obtaining explicit knowledge, spatial relations and interesting patterns from spatial data. Co-location pattern mining is one of the spatial data mining challenges. Spatial co-location pattern could be defined as a set of spatial objects or relationships which are frequently observed together in a spatial proximity. This work is mainly focused on development of a framework for co-location patterns mining in big spatio-temporal data. We also make evaluation of applied algorithms from the point of their efficiency and scalability.
机译：诸如Apache Hadoop和Apache Spark之类的现代大数据平台能够处理和分析庞大的数据集，但仍缺乏对空间数据分析的全面支持。然而，空间数据挖掘需要对大空间数据进行有效的分布式处理。空间数据挖掘是数据挖掘的一个子类，它主要致力于从空间数据中获取显式知识，空间关系和有趣的模式。协同定位模式挖掘是空间数据挖掘的挑战之一。空间共置模式可以定义为一组在空间邻近位置经常被一起观察到的空间对象或关系。这项工作主要集中在大时空数据中共址模式挖掘框架的开发上。我们还从应用程序算法的效率和可伸缩性的角度对其进行评估。

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