A novel true-real-time spatiotemporal data stream processing framework
Joint Authors
Source
Jordanian Journal of Computetrs and Information Technology
Issue
Vol. 8, Issue 3 (30 Sep. 2022), pp.256-270, 15 p.
Publisher
Princess Sumaya University for Technology
Publication Date
2022-09-30
Country of Publication
Jordan
No. of Pages
15
Main Subjects
Information Technology and Computer Science
Abstract EN
The ability to interpret spatiotemporal data streams in real time is critical for a range of systems.
However, processing vast amounts of spatiotemporal data out of several sources, such as online traffic, social platforms, sensor networks and other sources, is a considerable challenge.
The major goal of this study is to create a framework for processing and analyzing spatiotemporal data from multiple sources with irregular shapes, so that researchers can focus on data analysis instead of worrying about the data sources' structure.
We introduced a novel spatiotemporal data paradigm for true-real-time stream processing, which enables high-speed and low- latency real-time data processing, with these considerations in mind.
A comparison of two state-of-the-art real- time process architectures was offered, as well as a full review of the various open-source technologies for real- time data stream processing and their system topologies were also presented.
Hence, this study proposed a brand-new framework that integrates Apache Kafka for spatiotemporal data ingestion, Apache Flink for true- real-time processing of spatiotemporal stream data, as well as machine learning for real-time predictions and Apache Cassandra at the storage layer for distributed storage in real time.
The proposed framework was compared with others from the literature using the following features: Scalability (Sc), prediction tools (PT), data analytics (DA), multiple event types (MET), data storage (DS), Real-time (Rt) and performance evaluation (PE) stream processing (SP) and our proposed framework provided the ability to handle all of these tasks.
American Psychological Association (APA)
Angbera, Ature& Chan, Huah Yong. 2022. A novel true-real-time spatiotemporal data stream processing framework. Jordanian Journal of Computetrs and Information Technology،Vol. 8, no. 3, pp.256-270.
https://search.emarefa.net/detail/BIM-1435890
Modern Language Association (MLA)
Angbera, Ature& Chan, Huah Yong. A novel true-real-time spatiotemporal data stream processing framework. Jordanian Journal of Computetrs and Information Technology Vol. 8, no. 3 (Sep. 2022), pp.256-270.
https://search.emarefa.net/detail/BIM-1435890
American Medical Association (AMA)
Angbera, Ature& Chan, Huah Yong. A novel true-real-time spatiotemporal data stream processing framework. Jordanian Journal of Computetrs and Information Technology. 2022. Vol. 8, no. 3, pp.256-270.
https://search.emarefa.net/detail/BIM-1435890
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 268-270
Record ID
BIM-1435890