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Big Data, Scientific Programming, and Its Role in Internet of Industrial Things: A Decision Support System

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TLDR
The proposed study presents a decision support system to deal with big data and scientific programming for the Industrial Internet of Things and has used the tool of SuperDecisions to plot the hierarchy of situations and select the best alternative among the available.
Abstract
Big data is a challenging issue as its volume, shape, and size need to be modified in order to extract important information for a specific purpose. The amount of data is rising with the passage of time. This increase in volume can be a challenging issue to analyze the data for smooth industry and the Internet of things. Several tools, techniques, and mechanisms are available to support the handling and management process of such data. Decision support systems can be one of the important techniques which can support big data in order to make decisions on time. The proposed study presents a decision support system to deal with big data and scientific programming for the Industrial Internet of Things. The study has used the tool of SuperDecisions to plot the hierarchy of situations of big data and scientific programming and to select the best alternative among the available.

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Establishment of Trust in Internet of Things by Integrating Trusted Platform Module: To Counter Cybersecurity Challenges

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Protein-Protein Interaction Analysis through Network Topology (Oral Cancer).

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Cyber Security and Key Management Issues for Internet of Things: Techniques, Requirements, and Challenges

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References
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Journal ArticleDOI

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

TL;DR: This paper uses a systematic methodology to review the strengths and weaknesses of existing open-source technologies for big data and stream processing to establish their usage for Industry 4.0 use cases, and proposes some optimal combinations ofopen-source big data technologies for selected use cases.
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The role of big data analytics in industrial Internet of Things

TL;DR: In this paper, the authors investigated the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent Industrial Internet of Things (IIoT) systems and identified the indispensable challenges that remain to be addressed as future research directions as well.
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Industry 4.0 based process data analytics platform: A waste-to-energy plant case study

TL;DR: A process data analytics platform built around the concept of industry 4.0 that utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools, and takes advantage of the currently available industrial grade cloud computing platforms.
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Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies

TL;DR: A novel model is developed that summarizes the main capabilities of BDA in the context of manufacturing process and will help companies to understand the big data analytics capabilities and its potential implications for their manufacturing processes and support them seeking to design more effective BDA-enabler infrastructure.
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Machine learning based decision support systems (DSS) for heart disease diagnosis: a review

TL;DR: The study categorizes the ML techniques according to their performance in diagnosing various heart diseases, compares and evaluates the comparator based on physician’s performance, gold standards, other ML techniques, different models of same ML technique and studies with no comparison and investigates the current, future and no clinical implications.