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      Research Anthology on Big Data Analytics, Architectures, and Applications : 

      Efficient Big Data-Based Storage and Processing Model in Internet of Things for Improving Accuracy Fault Detection in Industrial Processes

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          Abstract

          As the lot of data is getting generated and captured in Internet of Things (IoT)—based industrial devices which is real time and unstructured in nature. The IoT technology—based sensors are the effective solution for monitoring these industrial processes in an efficient way. However, the real—time data storage and its processing in IoT applications is still a big challenge. This chapter proposes a new big data pipeline solution for storing and processing IoT sensor data. The proposed big data processing platform uses Apache Flume for efficiently collecting and transferring large amounts of IoT data from Cloud—based server into Hadoop Distributed File System for storage of IoT—based sensor data. Apache Storm is to be used for processing this real—time data. Next, the authors propose the use of hybrid prediction model of Density-based spatial clustering of applications with noise (DBSCAN) to remove sensor data outliers and provide better accuracy fault detection in IoT Industrial processes by using Support Vector Machine (SVM) machine learning classification technique.

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          The Internet of Things (IoT): Applications, investments, and challenges for enterprises

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            Is Open Access

            The industrial internet of things (IIoT): An analysis framework

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              The role of big data analytics in Internet of Things

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                Author and book information

                Contributors
                Book Chapter
                2022
                : 945-957
                10.4018/978-1-6684-3662-2.ch044
                674d74b0-23e7-4bd2-946a-9215f525e55c
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