With the rapid growth of georeferenced data, large-scale data processing and analysis methods are needed for spatial big data. Spatial co-location pattern mining is an interesting and important issue in spatial data mining area which discovers the subsets of features whose objects are frequently located together in geographic proximity. There are several works for efficiently processing co-location pattern discovery; however, they may be insufficient for large dense spatial data because the mining task takes up a lot of processing time and memory. In this work, we leveraged the power of a modern distributed computing platform, Hadoop, and developed an algorithm (called ParColoc) for parallel co-location mining on the MapReduce framework. This study explored challenge issues in designing the parallel co-location mining algorithm and solved them with adopting a spatial declusteirng technique and a NoSQL system. We conducted an experimental evaluation with real-world data and synthetic data to examine the effectiveness of proposed methods. The experiment result shows that ParColoc is a promising method for parallel co-location mining in cloud computing environment.

中文翻译：

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MapReduce和NoSQL系统的并行共置挖掘

随着地理参考数据的快速增长，空间大数据需要大规模的数据处理和分析方法。在空间数据挖掘领域中，空间共置模式挖掘是一个有趣且重要的问题，它发现了特征的子集，这些特征的对象通常在地理上邻近在一起。有许多工作可以有效地处理共址模式发现；但是，它们对于大型密集空间数据可能是不够的，因为挖掘任务会占用大量处理时间和内存。在这项工作中，我们利用了现代分布式计算平台Hadoop的功能，并开发了一种算法（称为ParColoc），用于在MapReduce框架上进行并行共址挖掘。这项研究探索了在设计并行托管服务器挖掘算法中的挑战性问题，并通过采用空间分解技术和NoSQL系统解决了这些问题。我们对真实数据和综合数据进行了实验评估，以检验所提出方法的有效性。实验结果表明，ParColoc是一种有前途的云计算环境并行托管挖掘方法。