Industrial Big Data Analytics: Challenges and Opportunities

Al-Abassi, Abdulrahman; Karimipour, Hadis; HaddadPajouh, Hamed; Dehghantanha, Ali; Parizi, Reza M.

doi:10.1007/978-3-030-38557-6_3

Cited by 16 publications

(8 citation statements)

References 49 publications

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“…Thus industrial data science, which is the study of industrial data to obtain insights that can typically lead to optimizing industrial applications, can play a vital role in such revolution. Manufacturing industries generate a large amount of data from various sources such as sensors, devices, networks, systems, and applications [6,68]. The main categories of industrial data include large-scale data devices, life-cycle production data, enterprise operation data, manufacturing value chain sources, and collaboration data from external sources [132].…”

Section: Real-world Application Domainsmentioning

confidence: 99%

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Sarker

2021

SN COMPUT. SCI.

162

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The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science" including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

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Section: Real-world Application Domainsmentioning

confidence: 99%

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Sarker

2021

SN COMPUT. SCI.

162

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“…V. BIG DATA CONCEPTS Big data (Bigdata) is characterized as a massive volume of data that needs modern computational technology and techniques to retrieve helpful data collection knowledge [38].…”

Section: ) (Nfc)nearmentioning

confidence: 99%

“…The IoT function of Big-Data is to process and archive vast volumes of data on a real-time basis using various storage technologies [38]. Extensive data analysis is evolving as a key to understanding IoT devices' data that helps to take the initiative to enhance decision-making.…”

Section: Need Of Integration Between Iot and Big Datamentioning

confidence: 99%

Internet of things and Big Data Analytic: A State of the Art Review

Hasan

2022

JASTT

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Th Connectivity plays a vital role in today's world when it comes to a device or things connection. The Internet of Things (IoT) plays a fundamental role in understanding real-world objects virtually and generating massive amounts of data from and for them. The exponential rise in data consumption and the exploding number of IoT-connected devices are only two indicators of how the expansion of Big-Data (BD) exactly parallels that of the IoT. Concerns of data collecting efficiency, data processing, analytics, and security are not trivial when it comes to managing big data in a constantly growing network. Researchers have looked at the difficulties of implementing the Internet of Things to find solutions to these issues. Despite the abundance of research on big data, analytics, and the Internet of Things, the intersection of these fields opens several prospects for developing big data and analytics in IoT settings. This paper focuses on feasible and ideal solutions for the effective integration of IoT with BD. The challenges of combining IoT and BD were also illustrated by presenting an analysis of this issue to a community of readers.

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“…Research challenges, opportunities and enabling technologies for Big Data analytics have been from the different perspectives in recent literature studies [22][23][24]. Big Data challenges can be grouped into three categories [22]: (1) data challenges related to data characteristics, e.g.…”

Section: Industrial Big Datamentioning

confidence: 99%

Challenges in Data Life Cycle Management for Sustainable Cyber-Physical Production Systems

Bekar

2020

IFIP Advances in Information and Communication Technology

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Rapid technological advances present new opportunities to use industrial Big Data to monitor and improve performance more systematically and more holistically. The on-going fourth industrial revolution, aka Industrie 4.0, holds the promise to support the implementation of sustainability principles in manufacturing. However, much of these opportunities are missed as social and environmental performance are still largely considered as an afterthought or add-on to business as usual. This paper reviews existing data life cycle models and discusses their usefulness for sustainable manufacturing performance management. Finally, we suggest possible directions for further research to promote more sustainable cyber-physical production systems.

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Industrial Big Data Analytics: Challenges and Opportunities

Cited by 16 publications

References 49 publications

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Internet of things and Big Data Analytic: A State of the Art Review

Challenges in Data Life Cycle Management for Sustainable Cyber-Physical Production Systems

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