On Data Stream Processing in IoT Applications

Namiot, Dmitry; Sneps-Sneppe, Manfred; Pauliks, Romass

doi:10.1007/978-3-030-01168-0_5

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Several windowing mechanisms will be discussed. Nevertheless, a windowing mechanism can be defined as a function of the time or the number of events [ 27 ]. A sliding window mechanism is defined as a window with a fixed size that slides over the data stream [ 26 ].…”

Section: Big Data Stream Processing Frameworkmentioning

confidence: 99%

“…After this initial study, we look for works that compare some of these frameworks to make an unbiased comparison. In 2015, Namiot et al [ 10 ] made an introductory comparison of the properties of Storm, Spark, Samza, Apache Flume, Apache Kafka, Amazon Kinesis, and IBM InfoSphere.…”

Section: Big Data Stream Processing Frameworkmentioning

confidence: 99%

“…However, we are more focused on data stream processing frameworks. In addition, the authors of [ 3 ] also discussed the concept of real-time associated with the processing of data streams, while the authors of [ 10 ] only perform a brief comparison of streaming processing frameworks. The authors of [ 10 ] conducted some practical evaluations of the streaming processing frameworks.…”

Section: Introductionmentioning

confidence: 99%

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Time series big data: a survey on data stream frameworks, analysis and algorithms

et al. 2023

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Big data has a substantial role nowadays, and its importance has significantly increased over the last decade. Big data’s biggest advantages are providing knowledge, supporting the decision-making process, and improving the use of resources, services, and infrastructures. The potential of big data increases when we apply it in real-time by providing real-time analysis, predictions, and forecasts, among many other applications. Our goal with this article is to provide a viewpoint on how to build a system capable of processing big data in real-time, performing analysis, and applying algorithms. A system should be designed to handle vast amounts of data and provide valuable knowledge through analysis and algorithms. This article explores the current approaches and how they can be used for the real-time operations and predictions.

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Section: Big Data Stream Processing Frameworkmentioning

confidence: 99%

Section: Big Data Stream Processing Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Time series big data: a survey on data stream frameworks, analysis and algorithms

et al. 2023

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“…In most cases, stream processing is associated with real-time processing. Therefore, stream data processing is a natural solution for Internet of Things (IoT) applications [17]. For example, typical real-time stream processing should include: event detection (collection, filtering, prediction, etc.…”

Section: Stream Data Processingmentioning

confidence: 99%

A Streaming Data Processing Architecture Based on Lookup Tables

et al. 2023

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Processing in memory (PIM) is a new computing paradigm that stores the function values of some input modes in a lookup table (LUT) and retrieves their values when similar input modes are encountered (instead of performing online calculations), which is an effective way to save energy. In the era of the Internet of Things, the processing of massive data generated by the front-end requires low-power and real-time processing. This paper investigates an energy-efficient processing architecture based on table lookup in phase-change memory (PCM). This architecture replaces logical-based calculations with LUT lookups to minimize power consumption and operation latency. In order to improve the efficiency of table lookup, the RISC-V instruction set has included extended lookup and data stream transmission instructions. Finally, the system architecture is validated by hardware simulation, and the performance of computing the fast Fourier transform (FFT) application is evaluated. The proposed architecture effectively improves the execution efficiency and reduces the power consumption of data flow operations.

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Improving Project Management for the Development of New Internet Applications

Nekrasova

Alekseeva

2022

Lecture Notes in Computer Science

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On Data Stream Processing in IoT Applications

Cited by 8 publications

References 15 publications

Time series big data: a survey on data stream frameworks, analysis and algorithms

Time series big data: a survey on data stream frameworks, analysis and algorithms

A Streaming Data Processing Architecture Based on Lookup Tables

Improving Project Management for the Development of New Internet Applications

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