000			06165cam a2200577Ii 4500
001			ocn907924107
003			OCoLC
005			20190328114811.0
006			m o d
007			cr cnu|||unuuu
008			150423s2015 mau ob 001 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dN$T _dIDEBK _dOPELS _dE7B _dYDXCP _dCDX _dEBLCP _dDEBSZ _dTEFOD _dOCLCF _dB24X7 _dCOO _dIDB _dLIV _dOCLCQ _dMERUC _dWRM _dU3W _dD6H _dINT _dOCLCQ _dCUY _dOCLCQ _dICG _dDKC
019			_a908074729
020			_a9780128006658 _q(electronic bk.)
020			_a012800665X _q(electronic bk.)
020			_z9780128005378
035			_a(OCoLC)907924107 _z(OCoLC)908074729
050		4	_aZA4240
072		7	_aLAN _x025000 _2bisacsh
082	0	4	_a025.04 _223
100	1		_aTalburt, John R., _eauthor.
245	1	0	_aEntity information life cycle for big data : master data management and information integration / _h[electronic resource] _cJohn R. Talburt, Yinle Zhou.
264		1	_aWaltham, MA : _bElsevier : _bMorgan Kaufmann, _c2015
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aVendor-supplied metadata.
520			_aEntity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data's impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics.
505	0		_aFront Cover; Entity Information Life Cycle for Big Data; Copyright; Contents; Foreword; Preface; THE CHANGING LANDSCAPE OF INFORMATION QUALITY; MOTIVATION FOR THIS BOOK; AUDIENCE; ORGANIZATION OF THE MATERIAL; Acknowledgements; Chapter 1 -- The Value Proposition for MDM and Big Data; DEFINITION AND COMPONENTS OF MDM; THE BUSINESS CASE FOR MDM; DIMENSIONS OF MDM; THE CHALLENGE OF BIG DATA; MDM AND BIG DATA -- THE N-SQUARED PROBLEM; CONCLUDING REMARKS; Chapter 2 -- Entity Identity Information and the CSRUD Life Cycle Model; ENTITIES AND ENTITY REFERENCES; MANAGING ENTITY IDENTITY INFORMATION.
505	8		_aENTITY IDENTITY INFORMATION LIFE CYCLE MANAGEMENT MODELSCONCLUDING REMARKS; Chapter 3 -- A Deep Dive into the Capture Phase; AN OVERVIEW OF THE CAPTURE PHASE; BUILDING THE FOUNDATION; UNDERSTANDING THE DATA; DATA PREPARATION; SELECTING IDENTITY ATTRIBUTES; ASSESSING ER RESULTS; DATA MATCHING STRATEGIES; CONCLUDING REMARKS; Chapter 4 -- Store and Share -- Entity Identity Structures; ENTITY IDENTITY INFORMATION MANAGEMENT STRATEGIES; DEDICATED MDM SYSTEMS; THE IDENTITY KNOWLEDGE BASE; MDM ARCHITECTURES; CONCLUDING REMARKS; Chapter 5 -- Update and Dispose Phases -- Ongoing Data Stewardship.
505	8		_aDATA STEWARDSHIPTHE AUTOMATED UPDATE PROCESS; THE MANUAL UPDATE PROCESS; ASSERTED RESOLUTION; EIS VISUALIZATION TOOLS; MANAGING ENTITY IDENTIFIERS; CONCLUDING REMARKS; Chapter 6 -- Resolve and Retrieve Phase -- Identity Resolution; IDENTITY RESOLUTION; IDENTITY RESOLUTION ACCESS MODES; CONFIDENCE SCORES; CONCLUDING REMARKS; Chapter 7 -- Theoretical Foundations; THE FELLEGI-SUNTER THEORY OF RECORD LINKAGE; THE STANFORD ENTITY RESOLUTION FRAMEWORK; ENTITY IDENTITY INFORMATION MANAGEMENT; CONCLUDING REMARKS; Chapter 8 -- The Nuts and Bolts of Entity Resolution; THE ER CHECKLIST.
505	8		_aCLUSTER-TO-CLUSTER CLASSIFICATIONSELECTING AN APPROPRIATE ALGORITHM; CONCLUDING REMARKS; Chapter 9 -- Blocking; BLOCKING; BLOCKING BY MATCH KEY; DYNAMIC BLOCKING VERSUS PRERESOLUTION BLOCKING; BLOCKING PRECISION AND RECALL; MATCH KEY BLOCKING FOR BOOLEAN RULES; MATCH KEY BLOCKING FOR SCORING RULES; CONCLUDING REMARKS; Chapter 10 -- CSRUD for Big Data; LARGE-SCALE ER FOR MDM; THE TRANSITIVE CLOSURE PROBLEM; DISTRIBUTED, MULTIPLE-INDEX, RECORD-BASED RESOLUTION; AN ITERATIVE, NONRECURSIVE ALGORITHM FOR TRANSITIVE CLOSURE; ITERATION PHASE: SUCCESSIVE CLOSURE BY REFERENCE IDENTIFIER.
505	8		_aDEDUPLICATION PHASE: FINAL OUTPUT OF COMPONENTSER USING THE NULL RULE; THE CAPTURE PHASE AND IKB; THE IDENTITY UPDATE PROBLEM; PERSISTENT ENTITY IDENTIFIERS; THE LARGE COMPONENT AND BIG ENTITY PROBLEMS; IDENTITY CAPTURE AND UPDATE FOR ATTRIBUTE-BASED RESOLUTION; CONCLUDING REMARKS; Chapter 11 -- ISO Data Quality Standards for Master Data; BACKGROUND; GOALS AND SCOPE OF THE ISO 8000-110 STANDARD; FOUR MAJOR COMPONENTS OF THE ISO 8000-110 STANDARD; SIMPLE AND STRONG COMPLIANCE WITH ISO 8000-110; ISO 22745 INDUSTRIAL SYSTEMS AND INTEGRATION; BEYOND ISO 8000-110; CONCLUDING REMARKS.
505	8		_aAppendix A -- Some Commonly Used ER Comparators.
504			_aIncludes bibliographical references and index.
650		0	_aBig data.
650		0	_aSemantic Web.
650		0	_aPattern recognition systems.
650		0	_aData mining.
650		7	_aLANGUAGE ARTS & DISCIPLINES _xLibrary & Information Science _xGeneral. _2bisacsh
650		7	_aBig data. _2fast _0(OCoLC)fst01892965
650		7	_aData mining. _2fast _0(OCoLC)fst00887946
650		7	_aPattern recognition systems. _2fast _0(OCoLC)fst01055266
650		7	_aSemantic Web. _2fast _0(OCoLC)fst01112076
655		4	_aElectronic books.
655		7	_aElectronic books. _2lcgft
700	1		_aZhou, Yinle, _d1986- _eauthor.
776	0	8	_iPrint version: _aTalburt, John R. _tEntity Information Life Cycle for Big Data : Master Data Management and Information Integration. _dBurlington : Elsevier Science, �2015 _z9780128005378
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128005378
999			_c247075 _d247075