(Received: 9-Mar.-2022, Revised: 2-May-2022 , Accepted: 23-May-2022)
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.

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