Abstract
Big Data helps facilitate information visibility and process automation in design and manufacturing engineering. It also helps analyze trends through analytics and predict inventory, manufacturing output and equipment lifespan and cycles, etc. This paper introduces Big Data, its characteristics and a number of issues of Big Data in design and manufacturing engineering. These issues include design and manufacturing data, Big Data benefits and impacts and its applications and opportunities. Methods, technologies and some technology progress around Big Data are presented in this study. General challenges of Big Data and Big Data challenges in design and manufacturing engineering are also discussed.
Highlights
Big Data and CharacteristicsThe McKinsey study defines Big Data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” Big Data in many sectors ranges from a few dozen Terabytes (TB: Approximately 1012 bytes) to multiple Petabytes (PB: Approximately 1015 bytes) (Minelli et al, 2013)
Analysis, feedback and visualization are the techniques of Big Data analytics
Big Data is large in volume, velocity, variety, value, variability and veracity
Summary
The McKinsey study defines Big Data as “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze.” Big Data in many sectors ranges from a few dozen Terabytes (TB: Approximately 1012 bytes) to multiple Petabytes (PB: Approximately 1015 bytes) (Minelli et al, 2013). Characteristics (Russom, 2011; Eaton et al, 2012; ORRT, 2012; Zikopoulos et al, 2011; Demchenko et al, 2013) of Big Data can be categorized into “6 Vs” They are: Volume, Velocity, Variety, Value, Variability and Veracity. It means data size such as Terabytes (TB), Petabytes (PB), Exabytes (EB: Approximately 1018 bytes), Zettabytes (ZB: Approximately 1021 bytes) and Yottabyte (YB: Approximately 1024 bytes). The economic value of different data varies depending upon both the source and its end use (Zaslavsky et al, 2012; Megahed and Jones-Farmer, 2013; Rajpathak and Narsingpurkar, 2013) This refers to the fact that data can be changed at times. The data we consider big today may not be considered big tomorrow because of the advances in data processing, storage and other system capabilities (Zaslavsky et al, 2012)
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