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008			210927s2021 sz | s |||| 0|eng d
020			_a9783030791049
024	7		_a10.1007/978-3-030-79104-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTS183 _b2021 EB
100	1		_aHill, Richard _eautor _9682228
245	1	0	_aGuide to Industrial Analytics : _bSolving Data Science Problems for Manufacturing and the Internet of Things _cby Richard Hill, Stuart Berry
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XXV, 275 páginas) _b172 ilustraciones, 108 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aTexts in Computer Science _x1868-095X
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
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	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _9163432 _aProcesos de fabricación
650		7	_2embne _9483083 _aInternet de los objetos
700	1		_aBerry, Stuart _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-79104-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI