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001			978-3-030-79104-9
003			DE-He213
005			20240423130058.0
007			cr nn 008mamaa
008			210927s2021 sz | s |||| 0|eng d
020			_a9783030791049 _9978-3-030-79104-9
024	7		_a10.1007/978-3-030-79104-9 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223
100	1		_aHill, Richard. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aGuide to Industrial Analytics _h[electronic resource] : _bSolving Data Science Problems for Manufacturing and the Internet of Things / _cby Richard Hill, Stuart Berry.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXXV, 275 p. 172 illus., 108 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aTexts in Computer Science, _x1868-095X
505	0		_a1. Introduction to Industrial Analytics -- 2. Measuring Performance -- 3. Modelling and Simulating Systems -- 4. Optimising Systems -- 5. Production Control and Scheduling -- 6. Simulating Demand Forecasts -- 7. Investigating Time Series Data -- 8. Determining the Minimum Information for Effective Control -- 9. Constructing Machine Learning Models for Prediction -- 10. Exploring Model Accuracy.
520			_aMonitoring and managing operational performance is a crucial activity for industrial and business organisations. The emergence of low cost, accessible computing and storage through the Industrial Internet of Things (IIoT) has generated considerable interest in innovative approaches to doing more with data. Data Science, predictive analytics, machine learning, artificial intelligence and the more general approaches to modelling, simulating and visualizing industrial systems have often been considered topics only for research labs and academic departments. This book debunks the mystique around applied data science and shows readers, using tutorial-style explanations and real-life case studies, how practitioners can develop their own understanding of performance to achieve tangible business improvements. Topics and features: Describes hands-on application of data-science techniques to solve problems in manufacturing and the IIoT Presents relevant case study examples that make use of commonly available (and often free) software to solve real-world problems Enables readers to rapidly acquire a practical understanding of essential modelling and analytics skills for system-oriented problem solving Includes a schedule to organize content for semester-based university delivery, and end-of-chapter exercises to reinforce learning This unique textbook/guide outlines how to use tools to investigate, diagnose, propose and implement analytics solutions that will provide the evidence for business cases, or to deliver explainable results that demonstrate positive impact within an organisation. It will be invaluable to students, applications developers, researchers, technical consultants, and industrial managers and supervisors. Dr. Richard Hill is a professor of Intelligent Systems, head of the Department of Computer Science, and director of the Centre for Industrial Analytics at the University of Huddersfield, UK. His other Springer titles include Guide to Vulnerability Analysis for Computer Networks and Systems and Big-Data Analytics and Cloud Computing. Dr. Stuart Berry is Emeritus Fellow in the Department of Computing and Mathematics at the University of Derby, UK. He is a co-editor of the Springer title, Guide to Computational Modelling for Decision Processes.
650		0	_aData mining.
650		0	_aBig data.
650		0	_aManufactures.
650		0	_aMachine learning.
650		0	_aComputer networks .
650		0	_aComputer science.
650	1	4	_aData Mining and Knowledge Discovery.
650	2	4	_aBig Data.
650	2	4	_aMachines, Tools, Processes.
650	2	4	_aMachine Learning.
650	2	4	_aComputer Communication Networks.
650	2	4	_aComputer Science.
700	1		_aBerry, Stuart. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030791032
776	0	8	_iPrinted edition: _z9783030791056
776	0	8	_iPrinted edition: _z9783030791063
830		0	_aTexts in Computer Science, _x1868-095X
856	4	0	_uhttps://doi.org/10.1007/978-3-030-79104-9
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c184795 _d184795