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008			210205s2021 gw | s |||| 0|eng d
020			_a9783030631390
024	7		_a10.1007/978-3-030-63139-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.Q36 _b2021 EB
100	1		_aDagnino, Aldo _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678784
245	1	0	_aData analytics in the era of the industrial internet of things _cby Aldo Dagnino
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVII, 133 páginas) _b61 ilustraciones, 53 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
505	0		_aChapter 1: Industrial Internet of Things Framework -- Chapter 2: Industrial Analytics -- Chapter 3: Machine Learning to Predict Fault Events in Power Distribution Systems -- Chapter 4: Analyzing Events and Alarms in Control Systems -- Chapter 5: Condition Monitoring of Rotating Machines in Power Generation Plants -- Chapter 6: Machine Learning Recommender for New Products and Services -- Chapter 7: Managing Analytic Projects in the IIoT Enterprise.
520	3		_aThis book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts' decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision making in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.
988			_aSpringer_Computer_2021
650		7	_2embne _9138985 _aInvestigación
650		7	_2embne _9483083 _aInternet de los objetos
710	2		_aSpringerLink
776	0	8	_iPrinted edition: _z9783030631383
776	0	8	_iPrinted edition: _z9783030631406
776	0	8	_iPrinted edition: _z9783030631413
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-63139-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _ea _zSI