Spatiotemporal Aspects of Big Data

2018
Saadia Karim, Tariq Rahim Soomro, S. M. Aqil Burney

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.

Atslēgas vārdi
Apache Hadoop, big data analytics, spatiotemporal, Samza, Storm, Spark, Flink
DOI
10.2478/acss-2018-0012
Hipersaite
https://sciendo.com/article/10.2478/acss-2018-0012

Karim, S., Soomro, T., Burney, S. Spatiotemporal Aspects of Big Data. Applied Computer Systems, 2018, Vol. 23, No. 2, 90.-100. lpp. ISSN 2255-8683. e-ISSN 2255-8691. Pieejams: doi:10.2478/acss-2018-0012

Publikācijas valoda
English (en)