<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Spatiotemporal Aspects of Big Data

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 Public License.

{"article-title":"Spatiotemporal Aspects of Big Data"}

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...

Spatiotemporal Aspects of Big Data

Online veröffentlicht: 31. Dez. 2018

Seitenbereich: 90 - 100

DOI: https://doi.org/10.2478/acss-2018-0012

Schlüsselwörter
Apache Hadoop, big data analytics, spatiotemporal, Samza, Storm, Spark, Flink

© 2018 Saadia Karim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Spatiotemporal Aspects of Big Data

Online veröffentlicht: 31. Dez. 2018

Seitenbereich: 90 - 100

DOI: https://doi.org/10.2478/acss-2018-0012

SchlüsselwörterApache Hadoop, big data analytics, spatiotemporal, Samza, Storm, Spark, Flink

© 2018 Saadia Karim et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Schlüsselwörter
Apache Hadoop, big data analytics, spatiotemporal, Samza, Storm, Spark, Flink