000			06016cam a2200625Ii 4500
001			ocn921301942
003			OCoLC
005			20190328114812.0
006			m o d
007			cr cnu|||unuuu
008			150917s2016 ne ob 000 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dN$T _dYDXCP _dOPELS _dUMI _dTEFOD _dOCLCF _dIDEBK _dEBLCP _dNLE _dCOO _dGGVRL _dDEBSZ _dLOA _dVGM _dOCLCQ _dVT2 _dU3W _dD6H _dCEF _dEZ9 _dOCLCQ _dWYU
019			_a922704626 _a926045941 _a929521561 _a1066600477
020			_a9780128029145 _q(electronic bk.)
020			_a0128029145 _q(electronic bk.)
020			_z9780128028810
020			_z0128028815
035			_a(OCoLC)921301942 _z(OCoLC)922704626 _z(OCoLC)926045941 _z(OCoLC)929521561 _z(OCoLC)1066600477
050		4	_aQA76.9.Q36
072		7	_aCOM _x000000 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aTalia, Domenico, _eauthor.
245	1	0	_aData analysis in the cloud : models, techniques and applications / _h[electronic resource] _cDomenico Talia, Paolo Trunfio, Fabrizio Marozzo.
264		1	_aAmsterdam, Netherlands : _bElsevier Ltd., _c2016.
264		4	_c�2016
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aComputer Science Reviews and Trends
588	0		_aVendor-supplied metadata.
504			_aIncludes bibliographical references.
520			_aData Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.
505	0		_aCover; Title Page; Copyright Page; Dedication; Contents; Preface; Chapter 1 -- Introduction to Data Mining; 1.1 -- Data mining concepts ; 1.1.1 -- Classification ; 1.1.1.1 -- Decision Trees ; 1.1.1.2 -- Classification with kNN ; 1.1.2 -- Clustering ; 1.1.2.1 -- Bayesian Classification ; 1.1.2.2 -- The K-Means Algorithm ; 1.1.3 -- Association Rules ; 1.2 -- Parallel and distributed data mining ; 1.2.1 -- Parallel Classification ; 1.2.2 -- Parallel Clustering ; 1.2.3 -- Parallelism in Association Rules ; 1.2.4 -- Distributed Data Mining ; 1.2.4.1 -- Meta-Learning.
505	8		_a1.2.4.2 -- Collective Data Mining 1.2.4.3 -- Ensemble Learning ; 1.3 -- Summary ; References; Chapter 2 -- Introduction to Cloud Computing; 2.1 -- Cloud computing: definition, models, and architectures ; 2.1.1 -- Service Models ; 2.1.2 -- Deployment Models ; 2.1.3 -- Cloud Environments ; 2.1.3.1 -- Microsoft Azure ; 2.1.3.2 -- Amazon Web Services ; 2.1.3.3 -- OpenNebula ; 2.1.3.4 -- OpenStack ; 2.2 -- Cloud computing systems for data-intensive applications ; 2.2.1 -- Functional Requirements ; 2.2.1.1 -- Resource Management ; 2.2.1.2 -- Application Management.
505	8		_a2.2.2 -- Nonfunctional Requirements 2.2.2.1 -- User Requirements ; 2.2.2.2 -- Architecture Requirements ; 2.2.2.3 -- Infrastructure Requirements ; 2.2.3 -- Cloud Models for Distributed Data Analysis ; 2.3 -- Summary ; References ; Chapter 3 -- Models and Techniques for Cloud-Based Data Analysis; 3.1 -- MapReduce for data analysis ; 3.1.1 -- MapReduce Paradigm ; 3.1.2 -- MapReduce Frameworks ; 3.1.3 -- MapReduce Algorithms and Applications ; 3.2 -- Data analysis workflows ; 3.2.1 -- Workflow Programming ; 3.2.2 -- Workflow Management Systems ; 3.2.3 -- Workflow Management Systems for Clouds.
505	8		_a3.3 -- NoSQL models for data analytics 3.3.1 -- Key Features of NoSQL ; 3.3.2 -- Classification of NoSQL Databases ; 3.3.3 -- NoSQL Systems ; 3.3.3.1 -- Dynamo ; 3.3.3.2 -- MongoDB ; 3.3.3.3 -- Bigtable ; 3.3.4 -- Use Cases ; 3.4 -- Summary ; References ; Chapter 4 -- Designing and Supporting Scalable Data Analytics ; 4.1 -- Data analysis systems for clouds ; 4.1.1 -- Pegasus ; 4.1.2 -- Swift ; 4.1.3 -- Hunk ; 4.1.4 -- Sector/Sphere ; 4.1.5 -- BigML ; 4.1.6 -- Kognitio Analytical Platform ; 4.1.7 -- Mahout ; 4.1.8 -- Spark ; 4.1.9 -- Microsoft Azure Machine Learning ; 4.1.10 -- ClowdFlows.
505	8		_a4.2 -- How to design a scalable data analysis framework in clouds 4.2.1 -- Architecture and Execution Mechanisms ; 4.2.2 -- Implementation on Microsoft Azure ; 4.3 -- Programming workflow-based data analysis ; 4.3.1 -- VL4Cloud ; 4.3.2 -- JS4Cloud ; 4.3.3 -- Workflow Patterns in DMCF ; 4.3.3.1 -- Single Task ; 4.3.3.2 -- Pipeline ; 4.3.3.3 -- Data Partitioning ; 4.3.3.4 -- Data Aggregation ; 4.3.3.5 -- Parameter Sweeping ; 4.3.3.6 -- Input Sweeping ; 4.3.3.7 -- Tool Sweeping ; 4.3.3.8 -- Combination of Sweeping Patterns ; 4.4 -- Data analysis case studies.
505	8		_a4.4.1 -- Trajectory Mining Workflow Using VL4Cloud.
650		0	_aQuantitative research.
650		0	_aData mining.
650		0	_aCloud computing.
650		7	_aCOMPUTERS _xGeneral. _2bisacsh
650		7	_aCloud computing. _2fast _0(OCoLC)fst01745899
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aQuantitative research. _2fast _0(OCoLC)fst01742283
655		4	_aElectronic books.
655		0	_aElectronic book.
700	1		_aTrunfio, Paolo, _eauthor.
700	1		_aMarozzo, Fabrizio, _eauthor.
776	0	8	_iPrint version: _aTalia, Domenico. _tData Analysis in the Cloud : Models, Techniques and Applications. _d: Elsevier Science, �2015 _z9780128028810
830		0	_aComputer science reviews and trends.
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128028810
856	4	0	_3ScienceDirect _uhttps://www.sciencedirect.com/science/book/9780128028810
999			_c247167 _d247167