文章基本信息

标题：Spatiotemporal Aspects of Big Data
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作者：Saadia Karim ; Tariq Rahim Soomro ; S. M. Aqil Burney 等
期刊名称：Applied Computer Systems
印刷版ISSN：2255-8691
出版年度：2018
卷号：23
期号：2
页码：90-100
DOI：10.2478/acss-2018-0012
出版社：Walter de Gruyter GmbH
摘要：Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient results and, therefore, many different types of frameworks have been introduced in cooperate world. In the present research, a qualitative approach is used to present the framework classification in two categories: architecture and features. Frameworks have been compared on the basis of architectural characteristics and feature attributes as well. These two categories project a significant effect on the execution of spatiotemporal data in big data. Frameworks are able to solve the real-time problems in less time of cycle. This study presents spatiotemporal aspects in big data with reference to several dissimilar environments and frameworks.
关键词：Apache Hadoop ; big data analytics ; spatiotemporal ; Samza ; Storm ; Spark ; Flink