Big Data Bibliography
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This document provides a bibliography on big data topics organized into 5 sections: 1) Big data applications 2) Innovation and change management 3) Data-driven marketing 4) Business intelligence 5) Distributed infrastructure The bibliography contains references such as books, articles, and case studies on subjects like recommender systems, data mining, analytics, database systems, and managing innovation.
![Big Data Bibliography --Radisson
[Geben Sie Text ein]
© Echtzeit GmbH 2013, all rights reserved
jason.radisson@echtzeit.net
1. Big Data Applications
3. Core Skills: Data-Driven Marketing
‘Actionable e-Customer Lifetime Value Model,
Diamonds in the Data Mine,’ Loveman, Harvard
Based on Markov Chains and Decision Trees,’
Business Review, May 2003
Paauwe et al, 2007
‘Economic Theory of Choice and the Preference
‘Algorithmic Framework for Performing CollaReversal Phenomenon,’ Grether&Plott, American
borative Filtering,’ Herlocker et al, Association of
Economic Review, 1979
Computing Machinery, 1999
Inside the Tornado: Strategies from Silicon Valley
Bayesian Reasoning &Machine Learning, Barber,
for Developing, Leveraging, and Surviving Hyper2012
growth Markets, Moore, 2004
eBay vs. Amazon, Casadeus-Masanell&Thaker,
Marketing Metrics: 50+ Every Executive Should
Harvard Business School Case 9-712-405, 2012
Master, Reibstein et al, 2006
‘Effectiveness of Different Recommender AlgoNew Direct Marketing: How to Implement A Profrithms in the Mobile Internet,’ Hegelich&Jannach,
it-Driven Db. Marketing Strategy, Shepard, 1999
Association for Advancement of AI, 2009
Paradox of Choice, Schwarz, 2003
Mahout in Action, Owen et al, 2011
Pricing & Revenue Optimization, Phillips, 2005
Mining of Massive Datasets, Rajaraman&Ullmann,
Science & Practice of Persuasion, Cialdini, 2001
2012
Why We Buy: The Science of Shopping, Underhill,
‘Personalization Privacy Paradox: Empirical Eval2008
uation of Information Transparency and Willingness to be Profiled Online for Personalization,’
4. Core Skills: Business Intelligence
Awad et al, MIS, 2006
Analytics at Work: Smarter Decisions, Better Re‘RACOFI: A Rule-Applying Collaborative Filtering
sults (2009) and Competing on Analytics (2007)
System,’ Andersen et al, IEEE, 2003
Davenport & Harris
Recommender Systems Handbook, Rokach, 2010
‘Big Data: What’s Your Plan?,’ McKinsey Quarter‘Value of Personalised Recommender Systems to
ly, Biesdorf et al, 2013
e-Business: a Case Study,’ Dias et al, RecSys, 2008
Decision Making Under Certainty/ Uncertainty,
Bell &Schleifer, 1995
2. Core Skills: Innovation &Change Management
Fundamental Methods of Mathematical Econom‘Cutting the Strategy Diamond in Highics, Chiang, 2006
Technology Ventures,’ California Management
Numbers Guide: The Essentials of Business NuReview, Burgleman& Siegel, 2008
meracy, Economist, 2003
‘Darwin & the Demon, Innovating Within EstabSay it with Charts: Executive’s Guide to Visual
lished Enterprises,’HBR, Moore, 2004
Communication, Zelanzny, 2001
Knowing-Doing Gap: How Smart Companies Turn
Visual Display of Quantitative Inform.,Tufte, 2001
Knowledge into Action,Pfeffer 2000
Valuation: Measuring and Managing the Value of
Innovators Dilemma: When New Technologies
Companies, Koller et al, 2010
Cause Great Firms to Fail, Christensen, 1997
Lessons of Experience: How Successful Execs De- 5. Core Skills: Distributed Infrastructure
velop on the Job, McCall, 1988
‘Big Table: A Distributed Storage System for
Let’s Get Real or Not Play, Khalsa, 2008
Structured Data,’ Chang et al, Operating Systems
Management 3.0: Leading Agile Developers, DeDesign and Implementation, 2006
veloping Agile Leaders, Appelo, 2010
HBase: The Definitive Guide, George, 2011
Simplicity Wins, Kluge et al, 1995
‘Map Reduce Simplified Data Processing on Large
Understanding & Changing Your Management
Clusters,’ Ghemawat, OSDI Proceedings, 2004
Style, Benfari, 1999
‘Parallel Database Systems: The Future of High
What Technology Wants, Kelly, 2011
Performance Database Processing,’ DeWitt &
Gray, CACM, 1992
Weird Ideas that Work: 11-1/2 Practices for
Promoting, Managing & Sustaining Innovation,
Patterns of Data Modeling, Blaha, 2010
Sutton, 2001
Seven Databases in Seven Weeks: A Guide to
Winning Through Innovation: A Practical Guide to
Modern Databases &NoSQL Movement, Redmond
Leading Organizational Change & Renewal,
& Wilson, 2012
O’Reilly &Tushman, 2002
‘The Google File System,’ Ghemawat et al, ACM
Proceedings, 2003](https://image.slidesharecdn.com/bigdatabibliography17-131103084149-phpapp01/85/bibliography-for-big-data-professionals-1-320.jpg)
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Big Data Bibliography
- 1. Big Data Bibliography --Radisson [Geben Sie Text ein] © Echtzeit GmbH 2013, all rights reserved jason.radisson@echtzeit.net 1. Big Data Applications 3. Core Skills: Data-Driven Marketing ‘Actionable e-Customer Lifetime Value Model, Diamonds in the Data Mine,’ Loveman, Harvard Based on Markov Chains and Decision Trees,’ Business Review, May 2003 Paauwe et al, 2007 ‘Economic Theory of Choice and the Preference ‘Algorithmic Framework for Performing CollaReversal Phenomenon,’ Grether&Plott, American borative Filtering,’ Herlocker et al, Association of Economic Review, 1979 Computing Machinery, 1999 Inside the Tornado: Strategies from Silicon Valley Bayesian Reasoning &Machine Learning, Barber, for Developing, Leveraging, and Surviving Hyper2012 growth Markets, Moore, 2004 eBay vs. Amazon, Casadeus-Masanell&Thaker, Marketing Metrics: 50+ Every Executive Should Harvard Business School Case 9-712-405, 2012 Master, Reibstein et al, 2006 ‘Effectiveness of Different Recommender AlgoNew Direct Marketing: How to Implement A Profrithms in the Mobile Internet,’ Hegelich&Jannach, it-Driven Db. Marketing Strategy, Shepard, 1999 Association for Advancement of AI, 2009 Paradox of Choice, Schwarz, 2003 Mahout in Action, Owen et al, 2011 Pricing & Revenue Optimization, Phillips, 2005 Mining of Massive Datasets, Rajaraman&Ullmann, Science & Practice of Persuasion, Cialdini, 2001 2012 Why We Buy: The Science of Shopping, Underhill, ‘Personalization Privacy Paradox: Empirical Eval2008 uation of Information Transparency and Willingness to be Profiled Online for Personalization,’ 4. Core Skills: Business Intelligence Awad et al, MIS, 2006 Analytics at Work: Smarter Decisions, Better Re‘RACOFI: A Rule-Applying Collaborative Filtering sults (2009) and Competing on Analytics (2007) System,’ Andersen et al, IEEE, 2003 Davenport & Harris Recommender Systems Handbook, Rokach, 2010 ‘Big Data: What’s Your Plan?,’ McKinsey Quarter‘Value of Personalised Recommender Systems to ly, Biesdorf et al, 2013 e-Business: a Case Study,’ Dias et al, RecSys, 2008 Decision Making Under Certainty/ Uncertainty, Bell &Schleifer, 1995 2. Core Skills: Innovation &Change Management Fundamental Methods of Mathematical Econom‘Cutting the Strategy Diamond in Highics, Chiang, 2006 Technology Ventures,’ California Management Numbers Guide: The Essentials of Business NuReview, Burgleman& Siegel, 2008 meracy, Economist, 2003 ‘Darwin & the Demon, Innovating Within EstabSay it with Charts: Executive’s Guide to Visual lished Enterprises,’HBR, Moore, 2004 Communication, Zelanzny, 2001 Knowing-Doing Gap: How Smart Companies Turn Visual Display of Quantitative Inform.,Tufte, 2001 Knowledge into Action,Pfeffer 2000 Valuation: Measuring and Managing the Value of Innovators Dilemma: When New Technologies Companies, Koller et al, 2010 Cause Great Firms to Fail, Christensen, 1997 Lessons of Experience: How Successful Execs De- 5. Core Skills: Distributed Infrastructure velop on the Job, McCall, 1988 ‘Big Table: A Distributed Storage System for Let’s Get Real or Not Play, Khalsa, 2008 Structured Data,’ Chang et al, Operating Systems Management 3.0: Leading Agile Developers, DeDesign and Implementation, 2006 veloping Agile Leaders, Appelo, 2010 HBase: The Definitive Guide, George, 2011 Simplicity Wins, Kluge et al, 1995 ‘Map Reduce Simplified Data Processing on Large Understanding & Changing Your Management Clusters,’ Ghemawat, OSDI Proceedings, 2004 Style, Benfari, 1999 ‘Parallel Database Systems: The Future of High What Technology Wants, Kelly, 2011 Performance Database Processing,’ DeWitt & Gray, CACM, 1992 Weird Ideas that Work: 11-1/2 Practices for Promoting, Managing & Sustaining Innovation, Patterns of Data Modeling, Blaha, 2010 Sutton, 2001 Seven Databases in Seven Weeks: A Guide to Winning Through Innovation: A Practical Guide to Modern Databases &NoSQL Movement, Redmond Leading Organizational Change & Renewal, & Wilson, 2012 O’Reilly &Tushman, 2002 ‘The Google File System,’ Ghemawat et al, ACM Proceedings, 2003