000			03358nam a22004575c 4500
988			_aSpringer_Robotics_2020
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020			_a9789811387593
024	7		_a10.1007/978-981-13-8759-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .B45 _b2020 EB
245	0	0	_aMultimedia big data computing for IoT applications : _bconcepts, paradigms and solutions _cedited by Sudeep Tanwar, Sudhanshu Tyagi, Neeraj Kumar
250			_aFirst edition
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2020
300			_a1 recurso en línea (XIV, 477 páginas) _b191 ilustraciones, 121 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aIntelligent Systems Reference Library _x1868-4394 _v163
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aMultimedia Big data computing for IoT -- Energy Conservation in MMBD Computing and IoT - A Challenge -- An Architecture for the Real-Time Data Stream Monitoring in IoT -- Deep learning for Multimedia data in IoT -- Random Forest based Sarcastic Tweet Classification using multiple feature Collection -- Peak Average Power Ratio reduction in FBMC using SLM & PTS techniques -- Intelligent Personality Analysis on Indicators in IoT-MMBD Enabled Environment -- Data Reduction in MMBD Computing -- Large Scale MMBD Management and Retrieval -- Data Reduction Technique for Capsule Endoscopy -- Multimedia Social Big Data: Mining -- Advertisement prediction in social media environment using big data framework.
520	3		_aThis book considers all aspects of managing the complexity of Multimedia Big Data Computing (MMBD) for IoT applications and develops a comprehensive taxonomy. It also discusses a process model that addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements, presenting case studies to demonstrate its application. Further, the book examines the layered architecture of MMBD computing and compares the life cycle of both big data and MMBD. Written by leading experts, it also includes numerous solved examples, technical descriptions, scenarios, procedures, and algorithms.
650		7	_2embne _9495511 _aDatos masivos
650		7	_2embne _9483083 _aInternet de los objetos
650		7	_2embne _aMultimedia _9147630
700	1		_aTanwar, Sudeep _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aTyagi, Sudhanshu _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aKumar, Neeraj _eeditor _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9789811387586
776	0	8	_iPrinted edition: _z9789811387609
776	0	8	_iPrinted edition: _z9789811387616
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-13-8759-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _cm _dz _feng _ggw _h0 _b12/2019 _eel _zSI