Big data collection and analysis for manufacturing organisations

Autor:	Pankaj Sharma, Erkki Jantunen, David Baglee, Jaime Campos
Rok vydání:	2017
Předmět:	CBM sub_databases Engineering ta214 ta213 business.industry Scale (chemistry) Big data Volume (computing) 02 engineering and technology General Medicine Data science Variety (cybernetics) sub_informationsystems manufacturing Time frame big data 020204 information systems 0202 electrical engineering electronic engineering information engineering Information system 020201 artificial intelligence & image processing business
Zdroj:	Sharma, P, Baglee, D, Campos, J & Jantunen, E 2017, ' Big Data Collection and Analysis for Manufacturing Organisations ', Big Data and Information Analytics, vol. 2, no. 2, pp. 127-139 . https://doi.org/10.3934/bdia.2017002
ISSN:	2380-6974 2380-6966
DOI:	10.3934/bdia.2017002
Popis:	Data mining applications are becoming increasingly important for the wide range of manufacturing and maintenance processes. During daily operations, large amounts of data are generated. This large volume and variety of data, arriving at a greater velocity has its own advantages and disadvantages. On the negative side, the abundance of data often impedes the ability to extract useful knowledge. In addition, the large amounts of data stored in often unconnected databases make it impractical to manually analyse for valuable decision-making information. However, an advent of new generation big data analytical tools has started to provide large scale benefits for the organizations. The paper examines the possible data inputs from machines, people and organizations that can be analysed for maintenance. Further, the role of big data within maintenance is explained and how, if not managed correctly, big data can create problems rather than provide solutions. The paper highlights the need to have advanced mining techniques to enable conversion of data into information in an acceptable time frame and to have modern analytical tools to extract value from the big datasets.
Databáze:	OpenAIRE
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