Leveraging the Opportunities of Big Data and the Industrial Internet in Engineering Asset Management Organisations

2016 ◽  
pp. 365-372
Author(s):  
A Koronios ◽  
J Gao ◽  
A Pishdad
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Industrial Internet

Big Data Analytics and Internet of Things in Industrial Internet in Former Soviet Union Countries

2019 ◽  
pp. 359-378
Author(s):  
Vardan Mkrttchian ◽  
Leyla Ayvarovna Gamidullaeva ◽  
Svetlana Panasenko ◽  
Arman Sargsyan
Keyword(s):  
Big Data ◽  
Soviet Union ◽  
Internet Of Things ◽  
Former Soviet Union ◽  
Big Data Analytics ◽  
Accounting Data ◽  
New Concepts ◽  
Industrial Internet ◽  
Potential Applications ◽  
Data Location

The purpose of this chapter is to explore the integration of three new concepts—big data, internet of things, and internet signs—in the countries of the former Soviet Union. Further, the concept of big data is analyzed. The internet of things is analyzed. Information on semiotics is given, and it reduces to the notion of internet signs. Context concepts and the contribution of big data, internet of things, and internet of signs to contextual simplification are analyzed. The chapter briefly outlines some potential applications of the integration of these three concepts. The chapter briefly discusses the contribution of the study and gives some extensions. These applications included continuous monitoring of accounting data, continuous verification and validation, and use of big data, location information, and other data, for example, to control fraudsters in the countries of the former Soviet Union.

scholarly journals Influence of Big Data in managing cyber assets

2019 ◽  
Vol 9 (4) ◽  
pp. 503-514
Author(s):  
Amit Mitra ◽  
Kamran Munir
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Design Methodology ◽  
Insurance Industry ◽  
Manufacturing Industries ◽  
Time Data ◽  
Content Type ◽  
Innovative Leadership ◽  
Data Flows ◽  
Desk Research

Purpose Today, Big Data plays an imperative role in the creation, maintenance and loss of cyber assets of organisations. Research in connection to Big Data and cyber asset management is embryonic. Using evidence, the purpose of this paper is to argue that asset management in the context of Big Data is punctuated by a variety of vulnerabilities that can only be estimated when characteristics of such assets like being intangible are adequately accounted for. Design/methodology/approach Evidence for the study has been drawn from interviews of leaders of digital transformation projects in three organisations that are within the insurance industry, natural gas and oil, and manufacturing industries. Findings By examining the extant literature, the authors traced the type of influence that Big Data has over asset management within organisations. In a context defined by variability and volume of data, it is unlikely that the authors will be going back to restricting data flows. The focus now for asset managing organisations would be to improve semantic processors to deal with the vast array of data in variable formats. Research limitations/implications Data used as evidence for the study are based on interviews, as well as desk research. The use of real-time data along with the use of quantitative analysis could lead to insights that have hitherto eluded the research community. Originality/value There is a serious dearth of the research in the context of innovative leadership in dealing with a threatened asset management space. Interpreting creative initiatives to deal with a variety of risks to data assets has clear value for a variety of audiences.

scholarly journals A Parallel Military Dog based Algorithm for Clustering Big data in Cognitive Industrial Internet of Things

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Ashish Kumar Tripathi ◽  
Kapil Sharma ◽  
Manju Bala ◽  
Akshi Kumar ◽  
Varun G Menon ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Industrial Internet Of Things ◽  
Industrial Internet

scholarly journals Artificial intelligence and cyber-physical mechanical processing systems in digital industry

2018 ◽  
Vol 226 ◽  
pp. 05001
Author(s):  
Yuri Kabaldin ◽  
Dmitrii Shatagin
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Mechanical Processing ◽  
Industrial Internet Of Things ◽  
Cnc Machines ◽  
Blockchain Technology ◽  
Future State ◽  
Industrial Internet ◽  
Technology Group ◽  
And Control

The article suggests new approaches to the creation and control of cyber-physical machining systems in the digital industry. Features of the use of artificial intelligence and the possibilities of big data for analyzing the current and future state of CNC machines are considered. A specialized neuromorphic chip is proposed, with the possibilities for deep learning, for control and sharing knowledge between CNC machines as part of a technology group. The features of the use of blockchain technology in digital industry with the use of the industrial Internet of things are shown.

scholarly journals A High-Order CFS Algorithm for Clustering Big Data

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Fanyu Bu ◽  
Zhikui Chen ◽  
Peng Li ◽  
Tong Tang ◽  
Ying Zhang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Big Data Analytics ◽  
Learning Model ◽  
Heterogeneous Data ◽  
High Order ◽  
Tensor Model ◽  
Industrial Internet ◽  
Deep Learning Model

With the development of Internet of Everything such as Internet of Things, Internet of People, and Industrial Internet, big data is being generated. Clustering is a widely used technique for big data analytics and mining. However, most of current algorithms are not effective to cluster heterogeneous data which is prevalent in big data. In this paper, we propose a high-order CFS algorithm (HOCFS) to cluster heterogeneous data by combining the CFS clustering algorithm and the dropout deep learning model, whose functionality rests on three pillars: (i) an adaptive dropout deep learning model to learn features from each type of data, (ii) a feature tensor model to capture the correlations of heterogeneous data, and (iii) a tensor distance-based high-order CFS algorithm to cluster heterogeneous data. Furthermore, we verify our proposed algorithm on different datasets, by comparison with other two clustering schemes, that is, HOPCM and CFS. Results confirm the effectiveness of the proposed algorithm in clustering heterogeneous data.

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