Spatiotemporal Aspects of Big Data

Abstract

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.

A Survey on Spatio-temporal Data Analytics Systems

“…Yao et al [266] studied and discussed recent technologies and techniques for big spatial vector data management based on the data model, storage, indexing, and processing and analysis. Karim et al [134] portrait the spatio-temporal aspect of big data and performed a comparison of the supported spatio-temporal features on diferent frameworks, such programming languages, and GIS software. We have studied and discussed major big data platforms based on the type of supports, such as spatial (vector, raster), spatio-temporal, trajectory, and spatial streams.…”

A Survey on Spatio-temporal Data Analytics Systems

“…Yao et al [258] studied and discussed recent technologies and techniques for big spatial vector data management based on the data model, storage, indexing, and processing and analysis. Karim et al [129] portrait the spatio-temporal aspect of big data and performed a comparison of the supported spatio-temporal features on different frameworks, such as Apache Hadoop [86] (SpatialHadoop), Apache Samza [166], Apache Storm [14], Apache Spark [85] (SpatialSpark and GeoSpark), and Apache Flink [92]. Almeida et al [57] presented a survey on big trajectory data analytics from the viewpoints of storage, processing, summarization, and analysis of trajectories.…”

Big Spatiotemporal Data Analytics: a research and innovation frontier

“…Spatiotemporal thinking (Yang et al 2011) integrating interdisciplinary knowledge through a spatiotemporal framework is important to harness the wealth of crossdomain knowledge and to fully understand Big Spatiotemporal Data (Yang et al 2017). New thinking uncovering hidden rules and new frameworks are needed to devise new methods so that patterns embedded can be revealed within improved conceptual frameworks (Karim et al 2018). Progress are being made, for example developing new methods, to analyze Big Spatiotemporal Data using machine learning for natural hazards (Martinez-Alvarez and Morales-Esteban 2019).…”

“…In the past decade, Big Spatiotemporal Data have driven and enabled innovations in all aspects of information systems from hardware, algorithms, software/tools, to applications and fostered the integration of different traditional disciplines to enable new research directions. Distinct from traditional data analytics, Big Spatiotemporal Data Analytics demands new frameworks and information attributes (Karim et al 2018) to obtain results more efficiently when discovering trends and patterns in various domains from human dynamics (Fang et al 2017), traffic congestion (He et al 2017), smart cities (Machado et al 2019), industry evolution , medical and health issues (Kraemer et al 2018), to brain science (Bassett and Sporns 2017).…”