Requirements for a big data capturing and integration architecture in a distributed manufacturing scenario

Nino, Mikel; Fernando, Saenz; Arbe, José Miguel Blanco; Illarramendi, Arantza

doi:10.1109/indin.2016.7819372

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…It is crucial to ensure data security when capturing and transmitting data to and from the cloud computing environment. Data access should be controlled with the help of authenticated mechanisms so that data collected from one plant cannot be visible to people of other plants [69] and transmission should be secured by various powerful encryption algorithms.…”

Section: Mine Construction Coal Mine Construction [129] Mineral Data ...mentioning

confidence: 99%

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

et al. 2023

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Technological advancements in large industries like power, minerals, and manufacturing are generating massive data every second. Big data techniques have opened up numerous opportunities to utilize massive datasets in several effective ways to improve the efficacy of related industries. This paper presents a review of big data technologies used in the power, mineral, and manufacturing industries for various purposes. We analyze the meta-data of the collected papers before reviewing and selecting papers by applying selection criteria and paper quality assessment strategy. Then we propose a taxonomy of big data application areas in the power, mineral, and manufacturing industries. We have studied current big data architectures and techniques implemented in industry sectors and have uncovered the big data research gaps in industry sectors. To address the gaps, we point out some relevant research questions and, to answer the questions, we make some future research recommendations that might explore interesting research ideas for building a big data-driven industry. As the careful use of big data benefits every other industry sector; hence, supportive big data frameworks need to be developed to speed up the big data analysis process. Proper multi-dimensional big data assessment is also needed to take into account for serving effective data analysis tasks.INDEX TERMS Big data for industry, smart grid, power system, oil and gas industry, minerals, big data technology, manufacturing industry, big-data-driven industry.

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Section: Mine Construction Coal Mine Construction [129] Mineral Data ...mentioning

confidence: 99%

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

et al. 2023

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“…To analyse the problems of the industry, a case study approach was used to identify integrational problems related to cloud-based big data analytics. The result presented requirements from a real-world business scenario perspective (Nino, Saenz, Blanco, & Illarramendi, 2017). The additional web-based research presents an architecture, which increases the awareness of the whole supply chain for interoperability reasons (Danila, Stegaru, Stanescu, & Serbanescu, 2016).…”

Section: Integration Of Big Data and Cloud Technologiesmentioning

confidence: 99%

Innovation landscape and challenges of smart technologies and systems – a European perspective

Micheler

Goh

Lohse

2019

Production & Manufacturing Research

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Smart technologies with higher degrees of autonomy will be essential to achieve the next breakthrough in both agility and productivity in future manufacturing systems, often referred to as Industry 4.0. However, the technologies will also bring substantial design and integration challenges and novelty risks to manufacturing businesses. The aim of this paper is to analyse the current landscape and to identify the challenges for introducing smart technologies into manufacturing systems in Europe. Expert knowledge from both industrial and academic practitioners in the field was extracted using an online survey. A workshop was used to triangulate and extend the survey results. The findings indicate three main challenges for the ubiquitous implementation of smart technologies in manufacturing are: i) the perceived risk of novel technologies, ii) the complexity of integration, and iii) the consideration of human factors. Recommendations are made based on these findings to transform the landscape for smart manufacturing. ARTICLE HISTORY

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“…45,46 Data analytics: Analysis procedures in IIoT systems are performed in several stages. 47 Data scientists produce intelligence and information from well-processed high-quality data. After the model is developed, model measurement tasks are put into execution by providing sample data sets and obtaining and placing attributes in the dataset by ranking them.…”

Section: Bd Analytics Architecture In 5g-driven Iiot Systemsmentioning

confidence: 99%

Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart cities

Mukherjee

Gupta

Rawlley

et al. 2022

Trans Emerging Tel Tech

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There has been an exponential growth in the number of low‐cost heterogeneous sensor devices that are connected to the internet in the existing infrastructure of smart cities in the past decade. These sensors and actuator devices are employed in various industries to collect invaluable data that greatly impacts business decisions. State‐of‐the‐art research is being carried out to process the high‐volume data collected in high velocity streams with high variability in order to draw meaningful insights to cater to business needs in various domains. Big data analytics finds diversified opportunities in 5G‐enabled Internet of Things (IoT) and industrial IoT environment to study the data patterns and produce new results which provides big organizations a conducive environment to take informed decisions. This article presents an exhaustive investigation of the various applications and algorithms of the big data analytics in 5G‐driven IoT and industrial IoT systems with a detailed taxonomy of the existing analytical systems and also the challenges specific to the applications in an IoT environment. A holistic understanding on the importance of security and privacy of big data during the development of smart city infrastructure in high velocity streaming process has been explained for a broad understanding of the big data streaming processes. A thorough analysis of important data mining algorithms such as classification, association rule mining, clustering, and prediction methods for big IoT data from the recent literature also has been presented to provide a clear understanding of existing issues in handling big data in 5G‐enabled IoT for the development of sustainable smart city infrastructure.

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Requirements for a big data capturing and integration architecture in a distributed manufacturing scenario

Cited by 12 publications

References 15 publications

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

A Comprehensive Review on Big Data for Industries: Challenges and Opportunities

Innovation landscape and challenges of smart technologies and systems – a European perspective

Leveraging big data analytics in 5G‐enabled IoT and industrial IoT for the development of sustainable smart cities

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