Therefore, challenges in IoT data stream processing span three dimensions: accuracy, latency and energy and LE-STREAM jointly addresses them. Finally, as IoT devices are often battery powered, processing tasks must be performed in an energy-efficient way. Several IoT applications are time sensitive, requiring fast data processing. Sensor data suffers from uncertainty and inconsistency issues, that can affect its accuracy. Data processing in IoT is challenging due to its dynamic and heterogeneous nature, and the massive amount of generated data. LE-STREAM is a framework for IoT data stream processing. The study also reports our ongoing study on a multilevel streaming analytics architecture that can serve as a guide for organizations and individuals planning to implement a real-time data stream processing and analytics framework.
With a view to addressing this issue, in this paper we present a taxonomy, a comparative study of distributed data stream processing and analytics frameworks, and a critical review of representative open source (Storm, Spark Streaming, Flink, Kafka Streams) and commercial (IBM Streams) distributed data stream processing frameworks. One of the challenges in developing a streaming analytics infrastructure is the difficulty in selecting the right stream processing framework for the different use cases. As the stream processing technology matures and more organizations invest in digital transformations, new applications of stream analytics will be identified and implemented across a wide spectrum of industries. Big data processing systems are evolving to be more stream oriented where each data record is processed as it arrives by distributed and low-latency computational frameworks on a continuous basis.
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