Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provides valuable information, such as which locations are frequently visited by a social media user, which locations are common for a social media user group, and which locations are socially important for a group of urban area residents. However, discovering socially important locations is challenging due to huge volume, velocity, and variety of social media datasets, inefficiency of current interest measures and algorithms on social media big datasets, and the need of massive spatial and temporal calculations for spatial social media analyses. In contrast, cloud computing provides infrastructure and platforms to scale compute-intensive jobs. In the literature, limited number of studies related to socially important locations discovery takes into account cloud computing systems to scale increasing dataset size and to handle massive calculations. This study proposes a cloud-based socially important locations discovery algorithm of Cloud SS-ILM to handle volume and variety of social media big datasets. In particular, in this study, we used Apache Hadoop framework and Hadoop MapReduce programming model to scale dataset size and handle massive spatial and temporal calculations. The performance evaluation of the proposed algorithm is conducted on a cloud computing environment using Turkey Twitter social media big dataset. The experimental results show that using cloud computing systems for socially important locations discovery provide much faster discovery of results than classical algorithms. Moreover, the results show that it is necessary to use cloud computing systems for analyzing social media big datasets that could not be handled with traditional stand-alone computer systems. The proposed Cloud SS-ILM algorithm could be applied on many application areas, such as targeted advertisement of businesses, social media utilization of cities for city planners and local governments, and handling emergency situations.

中文翻译：

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社交媒体大数据集上基于云计算的重要社交位置发现

社交重要地点是社交媒体用户在其社交媒体生命周期中经常访问的地方。发现具有社会重要性的位置提供了有价值的信息，例如社交媒体用户经常访问哪些位置，哪些位置对于社交媒体用户组来说是常见的，以及哪些位置对于一组市区居民来说具有社交重要性。然而，由于社交媒体数据集的数量、速度和种类繁多、当前对社交媒体大数据集的兴趣度量和算法效率低下，以及需要对空间社交媒体分析进行大量空间和时间计算，发现具有社会重要性的位置具有挑战性。相比之下，云计算提供了基础设施和平台来扩展计算密集型工作。在文献中，与社会重要地点发现相关的有限数量的研究考虑到云计算系统来扩展数据集大小并处理大量计算。本研究提出了一种基于云的 Cloud SS-ILM 的社交重要位置发现算法来处理大量和多样化的社交媒体大数据集。特别是，在本研究中，我们使用 Apache Hadoop 框架和 Hadoop MapReduce 编程模型来扩展数据集大小并处理海量空间和时间计算。所提算法的性能评估是在云计算环境下使用土耳其 Twitter 社交媒体大数据集进行的。实验结果表明，使用云计算系统进行社会重要位置发现比经典算法提供更快的结果发现。而且，结果表明，有必要使用云计算系统来分析传统的独立计算机系统无法处理的社交媒体大数据集。所提出的 Cloud SS-ILM 算法可以应用于许多应用领域，例如企业的定向广告、城市规划者和地方政府对城市的社交媒体利用以及处理紧急情况。