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Data-Driven Economy Literature (by Subject Matter)

1. Overview and Benefits of Big Data Aggour, K. (2012) “Illuminating the Importance of Big Data,” GE Global Research, July.

The author explains the magnitude and importance of the big data revolution, likening it to electrification. In particular, the author focuses on the way in which big data is implemented by GE, from medical images to data about gas turbines, with particular attention paid to Industrial Big Data. Aldhous, P. (2013) “Google’s Research Chief: the Power of Big Data,” New Scientist, Issue

2810, May. The author gives a brief recounting of an interview with Google’s chief data scientist, Peter Norvig. Discussed are the benefits of more data, applications of new data analytics tools, and some limitations to the “bigger, not more precise” approach of Big Data. Anderson, R. and Roberts, D. (2012) Big Data: Strategic Risks and Opportunities: Looking

Beyond the Technology Issues, Crowe Horwath White Paper, September. Banerjee, S. et al. (2011) “How Big Data Can Fuel Bigger Growth,” Outlook: the Journal of High-Performance Business No. 3

Barker, A. and Ward, J. S. (2013) “Undefined by Data: A Survey of Big Data Definitions,”

arXiv:1309.5821, September. Bollier, D. (2010) The Promise and Peril of Big Data, Washington D.C.: The Aspen Institute. Boyd, D. and Crawford, K. (2012) “Critical Questions for Big Data,” Information,

Communication & Society, 15(5), 662–679. Castro, D. and Mistra, J. (2013) “The Internet of Things,” Center for Data Innovation. The authors describe the constituent features of the so-called Internet of Things and their applications to real-world problems, focusing on specific examples and case studies. Also included are policy recommendations for how best to take advantage of these new technologies. Center for Data Innovations (2014) 100 Data Innovations, Information Technology and

Innovation Foundation, Washington, D.C., January. Chui, M., Löffler, M. Roberts, R.(2010) “The Internet of Things,” McKinsey Quarterly, March. Crawford, K., Gray, M. L. Miltner, K. (2014) Big Data (Special Section), International Journal

of Communication, Vol. 8. Cukier, K., and Mayer-Schönberger, V. (2013) Big Data: A Revolution That Will Transform How We Live, Work and Think. Boston/New York: Houghton Mifflin Harcourt.

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Davenport, T. H. (2013) “Turning our Data Sights Outward,” Wall Street Journal, January 8.

---- (2014) Big Data at Work: Dispelling Myths, Uncovering the Opportunities,

Harvard Business Review Press. Dean, J. Ghemaway, S. et al. (2012) “Living with Big Data: Challenges and Opportunities,”

Google, September 14.

Deutsch, T. (2014) “Recapping 2014: Significant Trends for Big Data,” IBM Data Magazine, March 28.

---- (2013) “Putting Big Data Myths to Rest,” IBM Data Magazine, August. ---- (2013) “Getting Past the Big Data Hype – and Backlash: Why arguments at both ends of the

spectrum are missing the point,” IBM Data Magazine, March. Dumbill, E. (2012) “What is Big Data? An Introduction to the Big Data Landscape,” O’Reilly

Media Inc., January 11. Erwitt, J. and Smolan, R. (2012) The Human Face of Big Data, Against All Odds Productions. Geanina, E. (2012) “Perspectives on Big Data and Big Data Analytics,” Database Systems

Journal vol. III, no. 4 Gurin, J. (2014) “Big Data: How Open Will the Future Be?, Open Data Now, March 20. ---- (2014) “Nine Big Lesson about Big Data,” Open Data Now, Data Sphere March 21, 2014 ---- (2013) Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and

Fast Innovation, McGraw-Hill. Halevi, G., Moed, H. Lane, J. et al. (2012) Special Issue on Big Data, Research Trends Issue 30,

September

Hand, E. (2011) “Google Research Guru Pushes ‘Big Data’,” Nature (online) July 21. Hardy, Q. (2014) “They Have Seen the Future of the Internet, and It Is Dark” New York Times,

July 5. Henschen, D. (2014) “Big Data Reaches Inflection Point,” Information Week March 20. Hidalgo, C. A. (2014) “Saving Big Data from Big Mouths,” Scientific American, April 29. Hilbert, M. (2013) “Big Data for Development: From Information- to Knowledge Societies,”

United Nations ECLAC January 15.

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Intel IT Center (2012) “Big Data 101: Unstructured Data Analytics,” A Crash Course on the IT

Landscape for Big Data and Emerging Technologies, June.

Kersten, M., Mangegold, S. Thanos, C. (2012) “Big Data: Introduction to the Special Theme,” ERCIM News Online Edition, April.

King, J. (2013) “UN Tackles Socio-­‐Economic Crises With Big Data,” Computerworld, June. Letouzé, E. (2012). Big Data for Development: Opportunities and Challenges (White paper).

New York: United Nations Global Pulse. Madhaven, J., Balakrishnan, S. Brisbin, K. et al. (2012) “Big Data Storytelling through

Interactive Maps,” IEEE. Manovich, L. (2012). Trending: The Promises and the Challenges of Big Social Data, in Gold,

M. (ed.), Debates in the Digital Humanities (pp. 460–476). Univ. of Minnesota Press. Mills, M. P. (2013) “Every Breath You Take: The age of all-seeing, all-knowing information

analytics is nearly upon us,” City Journal, July. Moitra, A. and LaComb, C. (2012) “A Big Data Overview,” GE Global Research, January. National Research Council (2013) Frontiers in Massive Data Analysis, National Ac. Press. Nicole, K. (2012). “Big Data Needs Eye Candy to Go Mainstream: Here's the iPad App to

Do It,” Forbes December.

Simplicity 2.0 (2013) “Why You Need to Care about the ‘Internet of Things’ Thing,” July. Tucker, P. (2014) The Naked Future Penguin. Qi, H. and Gani, A. (2012) “Research on mobile cloud computing: review, trends and

perspectives,” DICTAP Second International Conference.

World Economic Forum (2012) “Big Data, Big Impact New Possibilities for International Development.”

This report provides an overview of big data, its implications for various domains, and the new challenges it presents, specifically for privacy, ownership, and human capital. Special attention is paid to big data implications in finance, education, health, and agriculture, as well as big data initiatives in the non-profit domain. ---- (2012) Harnessing the Power of Big Data in Real Time through In-Memory Technology and

Analytics,” Global Information Technology Report, ch. 1.7. 4syth (2012) “For Big Data Analytics There’s No Such Thing as Too Big: The Compelling

Economics and Technology of Big Data Computing,” 4syth. Com, March.

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2. Data Economy as New Economic Cycle Arthur, W. B. (2011) “The Second Economy,” McKinsey Quarterly, October. This piece describes the economic revolution brought about by the emergence of a “second” digital economy alongside the physical economy, which goes “well beyond the use of computers, social media, and commerce on the Internet.” The author considers what this transformation will entail going forward, suggesting that by 2025 the emerging second economy will be at least as large as the 1995 physical economy. The impact, both positive and negative, on employment is also considered. Brown, B., Chui, M. Manyika, J. (2011) “Are you ready for the era of ‘big data’?,” McKinsey

Quarterly October. Boyd, D. and Crawford, K. (2011) “Six Provocations for Big Data,” A Decade in Internet Time:

Symposium on the Dynamics of the Internet and Society, September. Bughin, J., Chui, M. Manyika, J. (2010) “Clouds, big data, and smart assets: ten tech-enabled

business trends to watch,” McKinsey Quarterly, August. Cukier, K. (2010) “Data Data Everywhere,” The Economist. Cukier, K. and Mayer-Schönberger, V. (2013) “The Rise of Big Data,” Foreign Affairs,

May/June. OECD (2013) Exploring Data-Driven Innovation as a New Source of Growth: Mapping the

Policy Issues Raised by “Big Data,” Directorate for Science, Technology and Industry, Committee for Information, Computer and Communications Policy, June 18.

This report offers an overview of big data and the benefits it brings to various domains, from health care and utilities to transportation and government. The policy issues relating to big data are given special attention, specifically vis-à-vis privacy, open data, infrastructure, and cybersecurity. The Economist (2011) “Building with Big Data,” May. Jordan, M. I. (2011) “The Era of Big Data,” The International Society for Bayesian Analysis

Bulletin Vol. 18 No. 2 June. Mandel, M. (2013) “Can the Internet of Everything Bring Back the High-Growth Economy?”,

Progressive Policy Institute, September. Manyika, J. et al. (2011) "Big data: the next frontier for innovation, competition, and

productivity," McKinsey Global Institute, May. This piece discusses the growing size of the data universe, focusing on the overall amount of data in circulation across the economy, especially the data associated with smartphones, healthcare, and real-time position technologies. The economic value of these changes are calculated and discussed apropos specific sectors or the economy (especially health care and public sectors). Some predictions are then proffered, followed by a discussion of some obstacles faced by those hoping to take advantage of Big Data.

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Mehta, A. (2011) “Big Data: Powering the Next Industrial Revolution.” Tableau Software White Paper.

Mills, M. P. (2012) “The Next Great Growth Cycle,” The American, August. ---- (2012) “The Coming Tech-led Boom,” Wall Street Journal, January. ---- (2012) “Apps Lead the Way to the Next Innovation Hypercycle,” Forbes, December. ---- (2011) “Facebook, Groupon, Netflix Drive the Next Big Thing and America’s Economic

Resurgence,” Forbes, January. ---- (2012) Down the Digital Rabbit Hole as we Automate Everything, Forbes, October. President’s Council of Advisors on Science and Technology (2010) “Report to the President and

Congress, Designing a Digital Future: Federally Funded Research and Development in Networking and Information Technology,” December.

Schmidt, E. and Cohen, J. (2013) The New Digital Age: Reshaping the future of people, nations

and business. Knopf. 3. Big Data & Prediction Athanasopoulos, G. and Hyndman, R. J. (2011) “The Value of Feedback in Forecasting

Competitions,” March. This paper challenges traditional designs for forecasting competitions. The authors show how a new design, which gives instant feedback to competitors and allows them to revise and resubmit forecasts, can make forecasting more accurate. Asur, S. and Huberman, B. A. (2010) “Predicting the Future with Social Media,” Web

Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on , vol.1, no., pp.492-499, Aug. 31 -Sept. 3.

This paper illustrates how social media content can be used to predict real-world outcomes, focusing in particular on how Twitter may be used to predict box office revenues for movies. The authors demonstrate how a simple model using these data can outperform market-based predictors. Ayers, I. (2007) Super Crunchers: Why Thinking-By-Numbers is the New Way to Be Smart

Bantam Dell. The central argument of this book is that the rise of “number crunching” represents an important historic shift away from “intuitive and experiential expertise,” which is losing out time and time again. This is having important impacts on the expert industry, which remains composed of individuals “ordained because of their decades of individual trial-and-error experience,” instead of relying on databases to guide decision-making. The statistical analysis of big data for real-world decision-making – super-crunching -- is displacing and replacing traditional experts and changing our lives for the better. Backstrom, L. et al. (2010) "Find me if you can: improving geographical prediction with social

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and spatial proximity," Proceedings of the 19th international conference on World Wide Web.

Baker, P. (2013) “Big Data Prediction 'Fooled by Randomness' and Subject to TMI,” Fierce Big

Data, August. Davis, E. and Marcus, G. (2013) “What Nate Silver Gets Wrong,” The New Yorker January 25.

Davenport, T. H, and Harris, J. G. (2009) The Prediction Lover’s Handbook,” MIT Sloan

Management Review, January. ---- (2009) What People Want (and How to Predict It),” MIT Sloan Management Review, Winter. Dugas, A. F. et al. (2012) “Google Flu Trends: Correlation with Emergency Department

Influenza Rates and Crowding Metrics,” Clinical Infectious Diseases, January. Ferguson, A. G. (2014) “Big Data and Predictive Reasonable Suspicion,” 163 University of

Pennsylvania Law Review, April. This piece traces the history and context of the traditional legal framework for reasonable suspicion and the extent to which it remains viable in light of new data-related technologies and techniques used in policing. The author argues that the “new reality” of big data turns reasonable suspicion from a mechanism for protection into a justification for targeting suspects. Ginsberg, J. et al. (2008) “Detecting Influenza Using Search Engine Query Data.” Nature Graff, G. M. (2008) “Predicting the Vote: Pollsters Identify Tiny Voting,” Wired Magazine, June Kuang, C. (2008) “Tracking Air Fares: Elaborate Algorithms Predict Ticket Prices,” Wired

Magazine. June. Lazer, D. et al. (2014) “The Parable of Google Flu: Traps in Big Data Analysis,” Harvard Univ. Paynter, B. (2008) “Feeding the Masses: Data In, Crop Predictions Out,” Wired Magazine, June. Rogers, A. (2008) “Tracking the News: A Smarter Way to Predict Riots and Wars.” Wired

Magazine, June. Silver, N. (2012) The Signal and the Noise: Why So Many Predictions Fail – But Some Don’t.

Penguin.

Shmueli, G. (2010). To Explain or to Predict? Statistical Science, 25(3), 289–310. Sumner, C. et al. (2012) “Predicting Dark Triad Personality Traits from Twitter Usage and a

Linguistic Analysis of Tweets,” IEEE ICMLA. Thurm, S. (2011) “Next Frontier in Credit Scores: Predicting Personal Behavior” Wall Street

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Journal, October. 4. Big Data & Data Science Agarwal, S. et al. (2011)"Building Rome in a day," Communications of the ACM, 54:10,

pp. 105-­‐112. The authors present a system that can match and reconstruct 3-dimensional scenes from a large and varied collection of photographs found by searching for a given city, e.g., such as Rome, on Internet photo sharing sites. They demonstrate that it is now possible to reconstruct 3D models of cities consisting of 150k images in less than a single day.

Baker, P. (2013) Are Statisticians the Modern Explorers?,” Fierce Big Data, June. Banko, M. and Brill, E. (2001) “Scaling to Very Very Large Corpora for Natural Language

Disambiguation,” Microsoft Research. Bayesian Inference, “Big Data and Bayesian Inference,” Software Articles. Bramer, M. (2013) “Principles of Data Mining,” Springer. Crawford, K. (2013) “The hidden biases in big data. Harvard Business Review, April. Davenport, T. H. and Patil, D. J. (2012) “Will Big Data and Analytics Always Be with Us?”

The Wall Street Journal, December. Deutsch, T. (2013) “Don’t Overhype Data Science Expectations: Why It’s Important to Keep

Outsized Data Sciences Claims in Perspective,” IBM Data Magazine, April. Dhar, V. (2013) “Data Science and Prediction,” Communications of the ACM, Vol. 56 No. 12,

pp. 64-73 Di Justo, P. (2008) “Sorting the World: Google Invents New Way to Manage Data.” Wired

Magazine June. Dong, X. L., Halevy, A. Yu, C. (2009) “Data Integration with Uncertainty,” The VLDB

Journal 18:469–500. Drenik, G. (2014) “Going Beyond Big Data to Knowledge,” Forbes March. Feldman, R. (2013) “Techniques and Applications for Sentiment Analysis,” Communications of the ACM, 56:4, pp. 82-­‐89. This piece outlines the features and benefits of “sentiment analysis,” a technique that offers organizations the ability to “monitor various social media sites in real time and act accordingly.” The author shows how the use of this technique for picking stocks can lead to superior returns. Franklin, M., Halevy, A. Maier, D. (2005) “From Databases to Dataspaces: A New

Abstraction for Information Management,” SIGMOD Record, Vol. 34, No. 4, December.

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Graham, M. (2012) “Big data and the end of theory?”, The Guardian, March. Granger, C. W. J. (1998): “Extracting information from mega-panels and high-frequency data,”

Statistica Neerlandica, 52(3), 258–272. Halevy, A., Norvig, P., Pereira, F. (2009) “The Unreasonable Effectiveness of Data.” IEEE

Intelligent Systems, March/April pp. 8-12. Helland, P. (2011). “If You Have Too Much Data then “‘Good Enough’ Is Good Enough.”

Communications of the ACM, June p. 40. Horowitz, M. (2008) “Visualizing Big Data: Bar Charts for Words,” Wired Magazine June. International Year of Statistics (2014) Statistics and Science: A Report of the London Workshop

on the Future of the Statistical Sciences. Jeffery, K. et al. (2013)“A vision for better cloud applications,” Proceedings of the 2013

International Workshop on Multi‐Cloud Applications and Federated Clouds, Prague, Czech Republic, MODAClouds, ACM Digital Library, April 22-­‐23.

This paper provides an overview of the PaaSage project's approach to cloud applications. The authors argue that classical software engineering techniques no longer apply, but that PaaSage’s software engineering offers an alternative. Kinney, J. B. and Atwal, G. S. (2013) “Equitability, mutual information, and the maximal

information coefficient,” Crossmark, available through PNAS. Lam, J. et al. (2010) "Urban scene extraction from mobile ground based lidar data," Proceedings

of 3D PVT.. Le, Q.V. et al. (2012) “Building High-­‐level Features Using Large Scale Unsupervised Learning,”

Proceedings of the 29th International Conference on Mcahine Learning, Edinburgh, Scotland, UK.

Loredo, T. “The Perils and Promise of Statistics with Large Data Sets and Complicated

Models: Bayesian Frequentist Cross-Fertilization,” Cornell University. Narayanan, A. Shmatikov, V. (2008) “Robust De-anonymization of Large Sparse Datasets,”

Proceedings of the 2008 IEEE Symposium on Security and Privacy, pp. 111-125. ---- (2006) “How to Break Anonymity of the Netfliz Prize Dataset” ---- (2009) “De-­‐anonymizing social networks,” 30th IEEE Symposium on

Security and Privacy, pp. 173-­‐187.

Narayanan, A. et al. (2012) “On the Feasibility of Internet-­‐Scale Author Identification,” IEEE

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Symposium on Security and Privacy, May. National Science Foundation (2012): Core Techniques and Technologies for Advancing Big

Data Science & Engineering (BIGDATA), Solicitation 12-499. O’Neil, C. and Schutt, R. (2013) Doing Data Science: Straight Talk from the Frontline. O’Reilly

Media. Partridge, C. (2012) “Ontology Meets Big Data: Immutability,” 4th International Workship on

Ontology-Driven Information Systems Engineering. PhysOrg (2013) “Uncovering Hidden Structures in Massive Data Collections.” ---- (2014) “Researchers Propose a Better Way to Make Sense of 'Big Data'. Rajagopal, S. (2011) “Customer Data Clustering Using Data Mining Technique,” International

Journal of Database Management Systems ( IJDMS ) Vol.3, No.4, November. Rajaraman, A., Norvig, P. (1998) Virtual Database Technology: Transforming the Internet into

a Database,” IEEE Internet Computing July-August. Ramakrishnan, N., Grama, A. Y (1999) “Data Mining: From Serendipity to Science,” Computer,

August. Reeve, A. (2012) “Big Data and NoSQL: The Problem with Relational Databases,” EMC

InFocus, September. Reshef, D., et al. (2011) “Detecting Novel Associations in Large Data Sets.” Science, pp.

1518-24. Revolution Analytics (2011) “Advanced ‘Big Data’ Analytics with R and Hadoop,” White Paper. Rudin, C., et al. (2011) “21st Century Data Miners Meet 19th Century Electrical Cables.”

Computer, June, pp. 103-105.

Sarma, A. D. et al. (2009). “Representing Uncertain Data: Models, Properties, and Algorithms,” The VLDB Journal, October, Vol. 18, Issue 5, pp. 989-1019.

Sarma, A. D. et al. (2005) “Representing Uncertain Data: Uniqueness, Equivalence,

Minimization, and Approximation,” Technical Report, Stanford. Schumaker, R. P. (2011) “From Data to Wisdom: The Progression of Computational Learning in

Text Mining,” Communications of the International Information Management Association, 11(1): 39-48, January.

Scott, S. L. et al. (2013), “Bayes and Big Data: The Consensus Monte Carlo Algorithm,”

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EFaBBayes 250 Conference October 31, 2013. Sing, Y. J. et al. (2010) “Dynamic Management of Transactions in Distributed Real-Time

Processing System,” International Journal of Database Management Systems (IJDMS), Vol. 2, No. 2, May.

Sundsøy, P. R., et al. (2010) "Product adoption networks and their growth in a large mobile

phone network," Advances in Social Networks Analysis and Mining (ASONAM). Wasserstein, R. (2014) “8 Top Challenges Big Data Brings to Statisticians,” Fierce Big Data,

July. Wladawsky-Berger, I. (2014) “Why Do We Need Data Science When We’ve Had Statistics for

Centuries?,” USC Annenberg Innovation Lab. Zhang, Z. (2011) “When Bayes Meets Big Data,” March On Science Blog, July. 5. How Big is Big Data? Akamai (2014) “The State of the Internet,” Q4 2013 Report, Vol. 6 No. 4. Bohn, R. and Short, J. (2010) How Much Information? 2009 Report on Consumers, Global

Information Industry Center, University of California, San Diego. This report is a follow-up and improvement on the earlier Berkeley report (Lyman et al.) and estimates the amount of information consumed by Americans in 2008. To do so, the authors measure the flows of information (IFs), which include “every flow that is delivered to a person [is counted] as information,” but not stored data. Focus is given to IFs from television, and estimates are given for hours of consumption as compared to 1980. Short, J., Bohn, R. Baru, C. (2011) How Much Information? 2010 Report on enterprise

server information, Global Information Industry Center, Univ. of California, San Diego. Cisco (2013) “Zettabyte Era – Trends and Analysis,” White Paper May. Gantz, J. and Reinsel, D. (2011) “Extracting Value from Chaos,” IDC Report, June. This IDC report estimates the overall size of the “digital universe” (as exceeding the “zettabyte barrier”), offering several predictions for future growth. Attention is given to specific growth areas (individuals, enterprises) and proximate causes for the increases. The concept of the “digital shadow” is introduced and discussed, as are security and privacy concerns regarding Big Data and the digital universe. ---- (2012) The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest

Growth in the Far East, IDC Report, December. Gantz, J. et al. (2008) “The Diverse and Exploding Digital Universe: An Updated Forecast of

Worldwide Information Growth Through 2011” IDC Report (EMC). This is a follow-up to IDC’s inaugural forecast of the digital universe published in March 2007. The goal of this study is to calibrate the size and growth of the digital universe, focusing on the overall size, specific growth areas,

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storage and enterprise data; it also makes some predictions in these areas. Hilbert, M. (2011) “Mapping the dimensions and characteristics of the world’s technological

communication capacity during the period of digitization (1986 - 2007/2010),” International Telecommunication Union working paper, presented December.

Hilbert, M., Lopez, P. (2011) “The World’s Technological capacity to Store, Communicate, and

Compute Information.” Science 1 (April), pp. 60-65. ---- (2012) “How to Measure the World’s Technological capacity to Store, Communicate, and

Compute Information?” International Journal of Communication. International Telecommunication Union (2012) ”Measuring the Information Society.” Lyman, P. et al. (2003) “How Much Information? 2003”, U. C. Berkeley. This study estimates “the annual size of the stock of new information recorded in storage media and heard or seen each year in information flows” for the year 2002. (See Bohn/Short 2009 for a more recent study). The study also gives facts concerning how much information was produced by what sources (storage media, paper, World Wide Web, Telephones, etc.) and offers some qualifications about how to measure the data universe. OECD (2011), Guide to Measuring the Information Society. Turner, V. et al (2014) “The Digital Universe of Opportunities: Rich Data and the Increasing

Value of the Internet of Things,” IDC Report, (EMC) April. 6. Big Data & Security, Privacy Abelson, H. and Kagal, L. (2010) Access Control is an Inadequate Framework for Privacy

Protection, W3C Workshop on Privacy for Advanced Web APIs 12/13 (July) London. The authors argue that the current framework for evaluating and assessing privacy risks, “information access,” or the focus on how information comes to be known, is no longer viable given new big data-related technologies. Hence, they suggest, focus should instead be given to “how information is used,” following precedent, e.g., in Brandeis’s and Warren’s opinions about privacy. Acar, G. et al. (2013) “FPDetective: Dusting the Web for Fingerprinters,” Proceedings of CCS

November. The authors report on the design and implementation of a particular framework (FPDetective) for the detection and analysis of web-based fingerprints, i.e., identifying information for individuals based on web browser use. They conduct a large scale analysis of the million most popular websites and conclude that the adoption of fingerprinting is more widespread than previously thought, arguing that two common countermeasures to fingerprinting are insufficient. The authors suggest that a change in how users, companies, and policy-makers engage with fingerprinting is needed. Aggarwal, C. C. and Yu, P. S. (eds.) (2008) Privacy-Preserving Data Mining: Models and

Algorithms, Springer: New York. Al-Khouri, A. (2012) “Data Ownership: Who Owns My Data,” International Journal of

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Management & Information Technology, Vol. 2. No. 1, November Alstyne, M. V., Brynjolfsson, E. Madnick, S. (1995) “Why Not One Big Database? Principles

for Data Ownership,” Decision Support Systems Vol. 15 Iss. 4, December, pp. 267-284. Andrews, L. (2012). I Know Who You Are and I Saw What You Did: Social Networks and the

Death of Privacy. Simon and Schuster Billitteri, T. J. et al. (2013) “Social Media Explosion: Do social networking sites threaten

privacy rights?” CQ Researcher (January) 23:84-­‐104. Birnhack, M. (2013) “S-M-L-XL Data: Big Data as a New Informational Privacy Paradigm.” Butler, D. (2007). Data sharing threatens privacy. Nature News, 449(7163), 644–645. Cate, F. H. and Mayer-Schönberger, V. (2012) “Notice and Consent in a World of Big Data,”

Microsoft Global Privacy Summit Summary Report and Outcomes, November.

---- (2013) “Notice and consent in a world of Big Data,” International Data Privacy Law 3. Cavoukian, A. and Castro, D. (2014) Setting the Record Straight: De-Identification Does Work,

Information and Privacy Commissioner, Ontario Canada. Centre for Information Policy Leadership (2013) Big Data and Analytics: Seeking Foundations

for Effective Privacy Guidance,” A Discussion Document, February. Cisco (2013), “Solutionary Boosts Security with Cisco and MapR Technologies,” Customer

Case Study

Cloud Security Alliance (2014) “Big Data Working Group: Comment on Big Data and the Future of Privacy,” March.

Cukier, K and Mayer-­‐Schoenberger, V. (2014) "How Big Data Will Haunt You Forever,"

Quartz, March 11. Davenport, T. H. and Reidenberg, J. R. (2013) “Should the US Adopt European-Style Data

Privacy Protection?” Wall Street Journal, March. Davenport, T. H., Harris, J. G. (2007) “The Dark Side of Customer Analytics,” Harvard

Business Review, May. Driscoll, K. (2012). From Punched Cards to “Big Data”: A Social History of Database Populism.

communication 1, 1(1). Dwork, D. (2011) “A Firm Foundation for Private Data Analysis,” Communications of the

ACM, 54.1.

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This paper outlines a novel approach to data analysis, which aims to maintain the utility of databases while ensuring individual privacy. This approach, “differential privacy,” separates the utility of the database from individual data by randomizing responses “so as to effectively hide the presence or absence of the data of any individual.” Evans, B. J. (2011) “Much Ado about Data Ownership,” Harvard Journal of Law & Technology,

Vol. 25 No. 1. Fienberg, S.E. (2013) "Is the Privacy of Network Data an Oxymoron?" Journal of Privacy and

Confidentiality, 4:2. Gindin, S. E. (2009-2010) “Nobody Reads Your Privacy Policy or Online Contract: Lessons

Learned andQuestions Raised by the FTC's Action against Sears,” Northwestern Journal of Technology and Intellectual Property 1:8.

Gurin, J. (2013) “Open Data Trends: Cities, FOIA, and Open Science,” Open Data Now, March. Hardy, Q. (2014) “How Urban Anonymity Disappears When All Data Is Tracked,” The New

York Times, April. Harrison, T. et al. (2011) Open Government and E-Government: Democratic Challenges

from a Public Value Perspective Proceedings of the 12th Annual International Digital Government Research Conference, June 12–15.

Hart, D. (2000) “Data Ownership and Semiotics in Organizations, or Why ‘They're Not Getting

Their Hands on My Data!’” PACIS Proceedings, Paper 30. IBM (2013) Security Intelligence with Big Data IBM Institute for Business Value (2011) Opening up government: How to unleash the power

of information for new economic growth Global Business Services Executive Report. Information Age (2012) “Privacy, Smart Meters and the Internet of Things,” July. Khan, S. M. and Hamlen, K. W. (2012) “Anonymous Cloud: A Date Ownership Provider

Framework in Cloud Computing,” Proceedings of the 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications pp. 170-176.

Lane, J. et al. (2014) Privacy, Big Data, and the Public Good, Cambridge University Press. Lathrop, D. and Ruma, L. (2010) Open Government: Collaboration, Transparency, and

Participation in Practice (1st ed.). O’Reilly Media. Mackie, C. and Bradburn, N. (eds.) (2000) Improving Access to and Confidentiality of Research Data, National Research Council, Washington, D.C.

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Mundie, C. (2014) “Privacy Pragmatism: Focus on Data Use, Not Data Collection,” Foreign Affairs, March/April.

Nature Editorial (2007) A matter of trust. Nature, 449(7163), 637–638. ---- (2008) Community cleverness required. Nature, 455(7209), 1. Nissenbaum, H. (2009) Privacy in Context: Technology, Policy, and the Integrity of Social Life,

Stanford Law Books, November.

Noveck, B.S. (2009) Wiki government: how technology can make government better, democracy stronger, and citizens more powerful. Brookings Institution Press.

Rosen, Jeffrey (2011) “The Deciders: Facebook, Google, and the Future of Privacy and Free

Speech,” Constitution 3.0: Freedom and Technological Change, Rosen, J. and Wittes, B. (eds.) Brookings Institution Press: Washington D.C.

Rudin, C. (2013) “Predictive policing: Using Machine Learning to Detect Patterns of Crime,”

Wired, August. Saramäki, J. et al. (2014) “Persistence of social signatures in human communication,”

Proceedings of the National Academy of Sciences, 111.3:942-­‐947. Shields, G. (2010) Addressing Security and Data Ownership Issues When Choosing a SaaS

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Mills, M. P. (2012) “With the Tricorder X PRIZE Qualcomm Launches the New Era of Metadata

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Decision Trees and Bayesian Feature Selection.” Weinberger, S. (2008) “Spotting the Hot Zones: Now we Can Monitor Epidemics Hour by

Hour.” Wired Magazine, June.

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Wiens, J., Guttag, J. Horvitz, E. (2014) “A Study in Transfer Learning: Leveraging Data

from Multiple Hospitals to Enhance Hospital-­‐Specific Predictions,” Journal of the American Medical Informatics Association, January .

8. Big Data & Law Bringardner, J. (2008) “Winning the Lawsuit: Data Miners Dig for Dirt,” Wired Magazine, June. Elster, J. (2013) Big Data for Law Firms, Legal Management, October/November. Navetta, D. (2013) Legal Implications of Big Data: a Primer, ISSA Journal, March. Nelson, S. D. and Simek, J. W. (2013) “Big Data: Big Pain or Big Gain for Lawyers?” Law

Practice Vol. 39, No. 4. Walton, D. J. (2014) “Why Big Data is a Big Deal for Lawyers,” Inside Counsel, February.

9. Big Data & the Sciences Aiden, E. and Michel, J-P. (2013) Uncharted: Big Data as a Lens on Human Culture, New

York: Riverhead Books.

Akil, H. et al. (2011) “Challenges and Opportunities in Mining Neuroscience Data,” Science, 331, pp. 708-712, February.

The authors discuss how and why neuroscience requires the acquisition and integration of vast amounts of data of many types, arguing for a neuroinformatics approach to the study of the brain. The opportunities and challenges of data mining across multiple tiers of neuroscience information are discussed, and a case is made that cultural and infrastructural adaptation is necessary to profit from this approach. Anderson, C. (2008) “The end of theory: the Data Deluge Makes the Scientific Method

Obsolete,” Wired Magazine 16.07, June.

Wired’s Chris Anderson argues that Big Data renders models and, correspondingly, the scientific method, superfluous. In particular, large data sets (coupled with our ability to parse and analyze them) allow us to find novel correlations between otherwise diverse data sets; this, accordingly, allows us to dispense with causal or semantic questions in favor of dependable relations of correlation and prediction. Some examples of this are considered, such as J. Craig Venter’s biological research into bacteria. Blair, A. M. (2011) Too Much to Know: Managing Scholarly Information before the Modern

Age, New Haven: Yale University Press 2011. Curry, A. (2011) “Rescue of Old Data Offers Lesson for Particle Physics,” Science, 331, pp.

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Kitching, T. D., Rhodes, J. Heymans, C. (2012) “Image Analysis for Cosmology: Shape Measurement Challenge Review & Results from the Mapping Dark Matter Challenge,” April.

Koonin, S. E., Dobler, G. Wurtele, J. S. (2014) “Urban Physics,” American Physical Society

News, March. Lemonick, M. D. (2008) “Watching the Skies: Space is Really Big – But Not Too Bid to Map,”

Wired Magazine, June. Einav, L. and Levin, J. (2013) “The Data Revolution and Economic Analysis,” Working

Paper, No. 19035, National Bureau of Economic Research. This article discusses the ways in which big data will transform economic policy and economic research, focusing on how large-scale datasets can improve the measurement and monitoring of economic activity and the potential benefits of predictive modeling techniques. The challenges to accessing the relevant data are also discussed. McCulloch, E. S. (2013) “Harnessing the Power of Big Data in Biological Research,” American

Institute of Biological Sciences, Washington Watch, September. Norvig, P. (2009) “All We Want Are the Facts Ma’am,” Norvig.com Nuzzo, R. (2014) “Statistical Errors, P values, the ‘gold standard’ of statistical validity, are not as

reliable as many scientists assume.” Nature Vol. 506, 13 February. This piece argues that the traditional unit of statistical validity, the p-value, is put to work for which it was not intended. Hence researchers must be sensitive not only to the statistical significance of the relevant phenomenon studied (determined by the p-value), but also the plausibility and general significance of the hypothesis upon which the research is conducted. Partial solutions are considered, from Bayesian analyses to a more pluralistic methodology, especially emphasizing methodological transparency and broader scientific discussion – rather than forcing the numbers to do all the talking. These issues have relevance for big data insofar as the human element of sound judgment is shown to be indispensable to contextualize frame and evaluate data. Overpeck, J. T. et al. (2011) “Climate Data Challenges in the 21st Century,” Science 331,

pp. 700-702, February Saleem, M. et al. (2013) “Fostering Serendipity through Big Linked Data,” Semantic Web

Challenge. Siegfried, T. (2013) “Why Big Data is Bad for Science,” Magazine of the Society for Science and

the Public, November. Simon, H. A. (2002). “Science seeks parsimony, not simplicity: searching for pattern in

Phenomena,” in Zellner, A., Keuzenkamp, H. A. and McAleer, M. (eds.), Simplicity, Inference and Modelling: Keeping It Sophisticatedly Simple (pp. 32–72). Cambridge UP.

Von Baeyer, H. C. (2005) Information: The New Language of Science. Harvard UP.

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Vardi, M. (2012) “The Consequences of Machine Intelligence,” The Atlantic, October. The author argues that technology has been destroying jobs since the beginning of the Industrial Revolution. However, unlike previous technological advances, the Artificial Intelligence revolution is not continually creating new jobs. Instead, as smart machines compete, not with “human brawn” but with the “human brain” fewer jobs are fewer jobs will remain. By 2045, it is suggested, technological progress will be such that little employment will be left for humans.

Weidman, S. and Arrison, T. (2010) Steps toward Large Scale Data Integration in the Sciences,

National Research Council. This is a summary of a workshop held to outline best practices for large-scale data integration in the sciences. The report discusses case studies in various scientific fields, from astronomy to biology, as well as the relevant technologies. 10. Data and Enterprise Agarwal, R. et al. (2014) Enabling Big Data: Building the Capabilities that Really Matter, The

Boston Consulting Group Perspectives, May. Baker, P (2014) “How Big Data Changes the Way You Think and Operate,” Fierce Big Data

eBrief, April. Baker, P. and Gourley, B. (2014) Data Divination: Big Data Strategies, Cengage Learning PTR. Bertolucci, J. (2013) “Big Data’s New Buzzword: Datafication,” Information Week February. Castro, D. and Korte, T. (2013) “Data Innovation 101: An Introduction to the Technologies and

Policies Behind Data-Driven Innovation,” The Center for Data Innovation. This report provides an overview of how new technologies have improved storage, analytics, and communication of data, even while organizations have not embraced these tools as readily as they should. A call is made for policies to encourage data-driven innovation in both the public and private sectors. Cisco (2011) “Big Data in the Enterprise: Network Design Considerations.” ---- (2011) “Big Data in the Enterprise: Network Design Considerations.” White Paper. ---- “Cisco UCS with ParAccel Analytic Platform Solution: Deliver Powerful Analytics

to Transform Business.” Deighton, J. and Johnson, P. A. (2013) “The Value of Data: Consequences for Insight,

Innovation and Efficiency in the U.S. Economy,” Harvard Business School Columbia University, October.

DMI Mobile Enterprise Solutions (2014), “M2M and Big Data are Transforming the World,”

White Paper, April. Dora, S., Smit, S. Viguerie, P. (2011) “Drawing a new road for growth,” The McKinsey

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Quarterly. 2011.2 p. 12. Du Preez, D. (2012) “Big Data: hands on or hands off?”, Computing February 23. ---- (2011) “Big Data: How to Get the Board on Board,” Computing November. The Economist (2011) “Building with Big Data,” March 26. Gerhardt, B. et al. (2012) “Unlocking Value in the Fragmented World of Big Data Analytics:

How Information Infomediaries Will Create a New Data Ecosystem,” Cisco Internet Business Solutions Group, June.

IBM (2014) “Better Business Outcomes with IBM Big Data & Analytics,” Thought

Leadership White Paper, January. Linthicum, D. S. (2012) “Big Data Analytics Deep Dive: Deriving Meaning from the Data

Explosion,” Infoworld. This report provides an overview of the analytics tools, data sources, and the computing infrastructure that together make up big data. Special attention is paid to how businesses can benefit from big data analysis, focusing on specific cases, such as health care, retail, and transportation. Manyika, J. et al. (2013) “Open Data: Unlocking Innovation and Performance with

Liquid Data,” McKinsey Global Institute, October. This report outlines the benefits of open data in education, transportation, retail, energy, health, and finance. The authors estimate that these sectors of the economy could by themselves generate $3 trillion using open data. Policy recommendations are given for how to take advantage of open data while remaining sensitive to malfeasance. Manyika, J. and Roxburgh, C. (2011) “The Great Transformer: the Impact of the Internet on

Economic Growth and Prosperity, McKinsey Global Institute, October. The authors argue that the disruptive force of the Internet will have a net positive effect on the 21st century global economy. In particular, by fueling growth in disadvantaged regions of the world, empowering consumers, and creating jobs and whole new industries, the Internet economy offers new possibilities for social and economic betterment. There is much room for growth, however, especially in developing nations and in the public sector where productivity lags. Mills, M. P. (2012) “Rating Yelp as a Bellwether of the Social Media Tech Boom,”

Forbes, March. Provost, F. (2013) Data Science For Business: What You Need to Know about Data Mining and

Data-Analytic Thinking. O’Reilly Media. Saleh, T. et al. (2013) “The Age of Digital Ecosystems,” The Boston Consulting Group, July.

Schroek, M. et al (2012) The Real World Use of Big Data, IBM and the University of Oxford

Said School of Business.

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This report offers an overview of big data, distinguishing between various meanings given to the term. Specific big data initiatives are discussed and recommendations are given for how business can take further steps to adopt big data tools. The authors estimate that only 28% of organizations have implemented big data initiatives. Surdak, C. (2014) Data Crush: How the information tidal wave is driving new business

opportunities, AMACOM. Waters, R. (2013) “Data open doors to financial innovation,” Financial Times, December. Vesset, D. and Morris, H. (2013) Unlocking the Business Value of Big Data: Infosys

BigDataEdge, IDC. Vesset, D. and Villars, R. L. (2014) “Building a Datacenter Infrastructure to Support Your Big

Data Plans,” IDC sponsored by Cisco in collaboration with Intel January. ---- & Competition Angeles, S. (2014) “Big Data vs. CRM: How Can They Help Small Businesses?” Business News

Daily, March. This article provides an overview of Big Data and CRM (Customer Relations Management), defending the benefits of CRM. The relationship between Big Data and CRM is then outlined and a case is made for their mutual benefit, specifically for making small business more competitive. Brynjolffson, E., Hamerbacher, J. Stevens, B. (2011) “Competing through data: three experts

offer their game plans,” The McKinsey Quarterly, No. 4. Currie, B. (2014) “Using Big Data to Disrupt the World in Your Favor,” Campaign, March 9. Davenport, T. H. (2011) “Rethinking Knowledge Work: A Strategic Approach,” McKinsey

Quarterly, February. ---- (2007) Competing on Analytics: The New Science of Winning. Harvard Business

Press. Davenport, T. H., Harris, J. Shapiro, J. (2010) “Competing on Talent Analytics,” Harvard

Business Review, October. ----& Employment Autor, D. and Dorn, D. (2013) “How Technology Wrecks the Middle Class,” New York Times

August. The authors argue that increasing productivity, thanks to computerization and smart technology, is not resulting in reduced employment. However, as in previous historical stages of economic development, while increased productivity grows labor demand, it also results in replacing older high-skill jobs with newer lower-skill (e.g., automated) jobs. The result is a “polarization of employment, with job growth concentrated in both the highest- and lowest-paid occupations,” e.g., service jobs and abstract tasks-based jobs respectively, leaving fewer “in the middle.” The result of this “hollowing out” is thus not decreased employment but growing disparity in income and in low quality jobs.

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Besse, J. (2013) “Don’t Blame Technology for Persistent Unemployment,” The Denver Post

September 30. Brynjolfsson, E., and McAfee, A. (2011) Race Against The Machine: How the Digital

Revolution is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. Digital Frontier Press.

---- (2014) The Second Machine Age: Work, Progress, and Prosperity in

a Time of Brilliant Technologies, W. W. Norton & Company. Condon, B. and Wiseman, P. (2013) “Millions Of Middle-Class Jobs Killed by Machines In

Great Recession's Wake,” Huffington Post, January. ---- (2013) “Will Smart Machines Create a World without Work?”

Huffington Post, January. Condon, B., Fahey, J. and Wiseman, P. (2013) “Practically Human: Can Smart Machines Do

your Job?,” Huffington Post, January. This piece discusses the impact advances in big data, cloud computing, and smart technologies are having and will continue to have on employment. The authors argue that the outlook is grim: middle-class jobs are shrinking as humans are forced to compete with increasingly powerful, cheap, and easy-to-use machines, which can accomplish tasks once reserved for human hands.

Davenport, T. H. and Patil, D. J. (2012) “Data Scientist: The Sexiest Job of the 21st Century,” Harvard Business Review, 61, October.

Dillow, C. (2013) “The Big Data Employment Boom,” CNN Money, September. This article argues that big data is analogous to oil one century ago, except that the nature of the skills are higher tech. However, the author suggests that big data jobs will not, contrary to popular opinion, come only from people with technical backgrounds (for which there is a current and much-discussed shortfall) but will raise demand for managerial and decision-making skills, i.e., people from a wide range of backgrounds. Economist, The (2014) “The Onrushing Wave,” The Economist, January. Erlanger, L. (2009) “The Tech Jobs that the Cloud Will Eliminate,” InfoWorld, July. The author argues that over the next decade, as the cloud is fully integrated into the economy, it will “absorb functions traditionally done by IT.” This will result in the creation of some IT jobs in the short term, but will ultimately lead to huge employment losses in IT. Ezell, S. (2011). “Technology and Automation Create, Not Destroy, Jobs,” The Innovation Files,

January. Frey, C. B. and Osborne, M. (2013) “The Future of Employment: How Susceptible Are Jobs to

Computerisation?,” Oxford University, September.

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Frey, C. B. (2014) “Creative Destruction at Work,” Project Syndicate, July.

Goudreau, J.(2012) “Outlook 2012: Careers Headed for the Dustbin,” Forbes, February. This article argues that that employment losses in Great Recession were caused or aggravated by advancing IT, which will only lead to further jobs losses in the future, especially in middle-skill high school education-requisite jobs, and government jobs like the postal service. Kazt, L. & Margo, R. (2013) “Technical Change and the Relative Demand for Skilled Labor: the

United States in Historical Perspective.” Presented at Human Capital and History: The American Record” Conference, Cambridge MA, 2012.

Lynch, D. (2012) “It’s a Man versus Machine Recovery,” Bloomberg Business Week Magazine,

January. Mills, M. P. (2012) “Follow Splunk into the Big Data Revolution that Changes the Jobs

Equation,” Forbes, April. ---- (2012) “Amazon's Kiva Robot Acquisition is Bullish for Both Amazon and American Jobs,”

Forbes, March. Shacklett, M. (2013) “In-demand Big Data Skills: A Mix of Old and New,” Between the Line,

October. Smith, N. (2013) “The End of Labor: How to Protect Workers From the Rise of Robots” The

Atlantic, January. The author argues that technological advance will make human labor obsolete as smart machines take over hitherto un-automated cognitive skillsets. Unlike previous technological advances, new smart technologies will not merely augment human abilities, leading to greater productivity and employment; instead, smart machines will render superfluous human skills, even higher cognitive ones. This will result in a maximally efficient but inequitable society, as the only jobs left outside of the narrow elite will be in low skills and service domains. ---- & Management Baker, P. (2013) “Businesses Couple Intuition with Data for Better Outcomes,” Fierce Big Data,

June. Carlson, C. (2013) “Big Data Can't Supplant Instinct: Data Alone Can Lead to Efficiency, But

Probably Not Genius,” Fierce Big Data, March. Bradley, C., Hirt, M. Smit, S.(2011) “Have you tested your strategy lately?” The McKinsey

Quarterly, Winter Brynjolfsson, E. and McAfee, A. (2012) “Big Data: The Management Revolution,” Harvard

Business Review.

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The piece provides an overview of big data and focuses on its implications for business management. The authors argue that big data is transforming the nature of business management, specifically by allowing managers to “measure and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance.” Hence companies that are “born digital” will accomplish things that executives “could only dream of a generation ago” while traditional businesses stand to benefit by gaining competitive advantage. Also discussed are the obstacles, such as privacy challenges, to the widespread adoption of big data techniques. Davenport, T. H. and Morison, R. (2010) Analytics at Work: Smarter Decision, Better Results. Harvard Business Press. Davenport, T. H. and Snabe, J. H. (2011) “How Fast and Flexible Do You Want Your

Information, Really?” MIT Sloan Management Review, Spring. Emerald Publishing Group (2002) “Big Data needn’t be a big headache: How to tackle mind-

blowing amounts of information,” Strategic Direction Vol. 28 Iss: 8, pp. 22-24. Hopkins, B. and Evelson, B. (2011) “Expand Your Digital Horizon with Big Data,” Forrester,

September. Intel IT Center (2012) “Big Data Analytics: Intel’s IT Manager Survey on How Organizations

Are Using Big Data, August. Kelly, P. (2013) “Real-time network analytics can enable faster, more informed business

decisions,” Analysys Mason, July. Kerschbert, B. (2011) “Manufacturing Moneyball: Using Big Data and Business Intelligence to

Spur Operational Excellence,” Forbes, November. Madsbjerg, C. and Rasmussen, M. B. (2014) The Moment of Clarity: using the Human Sciences

to Solve Your Toughest Business Problems. Harvard Business Review Press. Rosenzweig, P. (2010) “Robert S. McNamara and the Evolution of Modern Management.”

Harvard Business Review, pp 87-93, December. ---- & Marketing Beales, J. H. and Eisenach, J. (2014) An Empirical Analysis Of The Value Of

Information Sharing in the Market for Online Content, Navigant Economics. Clifford, S. (2013) “Using Data to Stage-­‐Manage Paths to the Prescription Counter,” The New

York Times, June. Davenport, T. H., Mule, L. D. Lucker, J. (2011) “Know What Your Customers Want

Before They Do,” Harvard Business Review, December. Deutsch, T. (2013) “Real Time Versus Customer Time,” IBM Data Magazine, August.

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Etzioni, O. et al. (2003) “To Buy or Not to Buy,” Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 119-128

Hodgson, B. (2010) “A Vital New Marketing Metric: The Network Value of a Customer,”

Predictive Marketing: Optimize Your ROI with Analytics, September IBM (2014) “Big Data Ups the Customer Analytics Game,” Forrester Consulting, commissioned

by IMB, February. Ozimek, A. (2013) “Will Big Data Bring More Price Discrimination?” Forbes, September. Schiller, B. (2014) “First Degree Price Discrimination Using Big Data,” Brandeis Univ., Jan. U.S. Senate Committee on Commerce, Science & Transportation, (2013)

“A Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes,” December.

---- & Trade Business Software Alliance (2014) “Powering the Digital Economy: A Trade Agenda to Drive

Growth,” BSA Software Alliance. ---- (2013) 2013 BSA Global Cloud Computing Scorecard: A Clear Path to Success.” Castro, D. (2013) “The False Promise of Data Nationalism,” Information Technology and

Innovation Foundation, December Chander, A (2013) The Electronic Silk Road: How the Web Binds the World Together in

Commerce, New Haven: Yale University Press. Chander, A. and Le, U. P. (2014) “Breaking the Web : Data Localization vs. the Global

Internet,”California International Law Center, March. Christensen, L. et al. (2013). The Impact of the Data Protection Regulation

in the E.U. February. Cowhey, P and Kleeman, M. (2012) “Unlocking the Benefits of Cloud Computing for Emerging

Economies – A Policy Overview,” UC San Diego, School of International Relations and Pacific Studies.

Deloitte (2012) Measuring Facebook’s Economic Impact in Europe, January.

eBay, Inc. (2013) “Commerce 3.0 for Development: the promise of the Global Empowerment

Network,” based on an empirical study conducted by Sidley Austin LLP, October. ---- (2013) “Micro-Multinationals, Global Consumers and the WTO: Towards a 21st Century

Trade Regime.”

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---- (2014) “Technology-Enabled Global Trade: The Opportunities and Challenges on the Road

to 21st Century Commerce,” May.

European Commission (2013) “Restoring Trust in EU-US Data Flows,” Memo, November. Ezell, S. J., Atkinson, R. D., Wein, M. A. (2013) “Localization Barriers to Trade:

Threat to the Global Innovtaion Economy, Information Technology and Innovation Foundation, September.

Fleming, J. (2013) “Booming data economy puts EU to the test,” EurActiv, April. Gresser, E. (2014) “21st Century Trade Policy: The Internet and the Next generation’s Global

Economy,” Progressive Economy January. ---- (2012) Lines of Light: Data Flows as a Trade Policy Concept, Progressive Economy, May. Hamel, M-P. and Marguerit, D. (2013) “Analyse des big data: Quels usages, quels défis?”

Commissariat général à la stratégie et à la prospective No. 8, November. Hofheinz, P. and Mandel, M. (2014) “Bridging the Data Gap: How Digital Innovation Can Drive

Growth and Create Jobs,” Progressive Policy Institute Policy Brief, Issue 15. Kommerskollegium, Sweden National Board of Trade (2012) “E-commerce – New

Opportunities, New Barriers: A Survey of E-commerce Barriers in Countries outside the EU,” November.

---- (2013) “No Transfer, No Trade: the Importance of Cross-Border Data Transfers for

Companies Based in Sweden, January. Lee-Makiyama, H. (2014) “The Costs of Data Localization: Friendly Fire on Economic

Recovery,” European Centre for International Political Economy, April. London Economics (2013) “Implications of the European Commission’s Proposal for a General

Data Protection Regulation for Business.” Mandel, M. (2013) “Data, Trade, and Growth,” Progressive Policy Institute Working Paper,

February/ May. ---- (2013) “Data, Trade, and Growth,” Mack Center for Technological Innovation, presented

March. ---- (2012) “Beyond Goods and Services: The (Unmeasured) Rise of the Data-Driven Economy,”

Progressive Policy Institute Policy Memo, October. ---- (2014) “Data, Trade and Growth,” Progressive Policy Institute Policy Memo, April.

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Meltzer, J. (2013) “The Internet, Cross-Border Data Flows and International Trade,” Issues in

Technology Innovation, No. 22, February. ---- (2014) “Supporting the Internet as a Platform for International Trade: Opportunities for

Small and Medium-Sized Enterprises and Developing Countries,” The Brookings Institution.

Microsoft (2014) "Big and Open Data in Europe: A growth engine or missed opportunity?"

Presented at the European Parliament in Brussels, January 29th. Mulligan, M. and Card, D. (2014) “Sizing the EU App Economy,” Gigaom Research, February. OECD (2013) “Internet Traffic Exchange: Market Development and Policy Challenges,”

Directorate for Science, Technology and Industry, Committee for Information, Computer and Communications Policy, January.

Reding, V. (2014) A Data Protection Compact for Europe, by the Vice-President of the

European Commission, EU Justice Commissioner, January. US Chamber of Commerce (2013) “The Economic Importance of Getting Data Protection Right:

Protecting Privacy, Transmitting Data, Moving Commerce,” prepared by the European Centre for International Political Economy, March.

---- (2014) “Business without Borders: The Importance of Cross-Border Data Transfers to Global

Prosperity.” U.S. Department of Commerce (2014) “Digital Economy and Cross-Border Trade: the Value of

Digitally-Deliverable Services,” January. U.S. International Trade Commission (2013) “Digital Trade in the U.S. and Global, Economies,

Part 1,” July. World Trade Organization (2013), “E-Commerce in Developing Countries: Opportunities and

Challenges for Small Medium-Sized Enterprises.” 11. Data & Industry Applications Borovick, L. and Villars, R. L. (2012) “The Critical Role of the Network in Big Data

Applications,” IDC sponsored by Cisco Systems, February. Cisco (2012) “Cisco, Fusion-io, and Oracle Deliver Extreme Performance to Oracle NoSOL

Database Big Data Applications. Solution Brief,” Data Center and Internet Business Solutions Group (IBSG), September.

Goodwin, B. (2013) “Tesco Uses Big Data to Cut Cooling Costs by up to €20M ,” Computer

Weekly, May.

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Guyon, I. et al. (2012) “ChaLearn Gesture Challenge: Design and First Results,” Computer

Vision and Pattern Recognition Workshops (CVPRW), IEEE Computer Society Conference.

Kasprowski, P., Komogortsev, O. V. Karpov, A. (2012) “First Eye Movement Verification

and Identification Competition,” Proceeds of the IEEE Fifth International Conference on Biometrics.

Khaleghi, B. et al. (2013)“Multisensor data fusion: A review of the state-­‐of-­‐the-­‐art,”

Information Fusion, 14:1, pp. 28-­‐44. This paper offers a review of the data fusion state-of-the-art, focusing on its benefits and challenges. Moreover, possible directions for future research in the data fusion community are sketched. Levy, S. (2013) "Nest Gives the Lowly Smoke Detector a Brain," Wired, October. McKinsey & Company (2011) “Inside P&G’s Digital Revolution,” McKinsey Quarterly. Michel, J-B. et al. (2010). “Quantitative Analysis of Culture Using Millions of Digitized Books,”

Science, December. Software Information and Industry Association (2013) “Data-Driven Innovation: Understanding

and Enabling the Economic and Social Value of Data.” This report offers case studies on data-driven innovation from a wide range of industries. Policy recommendations are suggested for privacy, open data, neutrality, and public-private partnerships and a call is made to avoid “curbing” data collection and analysis as policymakers seek new regulations. ---- Energy Mills, M. P. (2013) “The Cloud Begins with Coal: Big Data, Big Networks, Big Infrastructure,

and Big Power,” National Mining Association & American Coalition for Clean Coal Electricity, April

---- (2013) “Big Data and Microseismic Imaging Will Accelerate the Smart Drilling Oil & Gas

Revolution,” Forbes, Mary. ---- (2012) “Big Data Unleashes the Electric Equivalent of a Free Keystone Pipeline,” Forbes,

March. Oracle (2012) Big Data, Bigger Opportunities: Plans and Preparedness for the Data Deluge. The report measures utilities companies’ preparedness for the advent of big data and smart grids. The authors promote using meter data management systems in order to better prepare as well as other recommendations for implementing analytics for use on smart grid data and decision-making. ---- (2013) “Utilities and Big Data: Accelerating the Drive to Value.”

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Waltz, D. and King, J. (2009) Information technology and America’s Energy Future, Computing Research Associate White Paper, Washington D.C.

----- Finance Bankingtech (2012) “Thinking Outside the Bank: When can banks learn from other industries

about how to create a culture of innovation in technology and services?”, Banking Technology, February.

Groenfeldt, T. (2012) “Data Daze (the phenomenon of ‘big data’ in financial institutions)”,

Banking Technology, February.

---- Industrial Brust, A. (2012) “Industrial Big Data,” Big on Data (ZDnet) April. Desai, P. (2013) “Big Data and Analytics at Work,” GE Global Research, November. GE (2012) “The Rise of Industrial Big Data,” White Paper. ---- (2013) “Big Data Meets 3-D Printing: Big Data to Monitor Laser-Printed Jet Engine Parts,”

GE Reports, June 4. ---- (2012) “GE Solves Industrial Big Data Problems with Tools of Today,” GE Intelligent

Platforms, October. ---- (2012) “GE Takes on Industrial Big Data Challenges,” GE Intelligent Platforms, August.

Olavsrud, T. (2013) “Big Data Will Drive the Industrial Internet,” CIO June. ---- Insurance Brat, E. et al. (2013) “Big Data: The Next Big Thing for Insurers?,” Boston Consulting Group,

March. Scism, L. and Maremont, M. (2010) “Inside Deloitte’s Life-Insurance Assessment Technology.”

Wall Street Journal, November. ---- (2010) “Insurers Test Data Profiles to Identify Risky Clients,” Wall Street Journal, Nov.

---- Public Sector Ferranti, D.M.D. (2009) How to improve governance: a new framework for analysis and

action. Brookings Institution Press. Goldstein, B. and Dyson, L. (2013) Beyond Transparency: Open data and the future of civic

innovation. Code for American Press. Harrison, T. et al. (2011) Open Government and E-Government: Democratic Challenges

from a Public Value Perspective.

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Kim, G.-H. et al. (2014) “Big-Data Applications in the Government Sector,” Communications

of the ACM Vol. 57 No. 3, March. Kum, H.-C., Ahalt, S. Carsey, T. M. (2011). “Dealing with Data: Governments Records,”

Science. National Association of State CIOs (2012) “Is Big Data a Big Deal for State Governments?” This report offers an overview of big data with a view to its relevance for state government actors. Recommendations are given for how to govern and monitor big data initiatives as well as how to tackle public-private partnerships that take advantage of data techniques. Open Government Partnership (2011) Open Government Declaration, September. Partnership for Public Service and IBM (2013) From Data to Decisions III: Lessons from Early Analytics Programs, November. This report outlines the growth of government analytics programs and offers recommendations for future development in the areas of inter-agency relations, cost savings, communication, and skills training. Tech America Foundation (2012) Demystifying Big Data: A practical guide to transforming

the business of government, prepared for Federal Big Data Commission. This report overs an overview and definition of big data, with numerous case studies, focusing on the benefits and challenges governments will face as they embrace these new technologies. Specific recommendations are given for how to implement big data strategies. The White House (2012) Digital Government: building a 21st century platform to better serve

the American people. ---- (2009) Transparency and Open Government: Memorandum for the Heads of Executive

Departments and Agencies. Yiu, C. (2012) The Big Data Opportunity: Making governments faster, smarter and more

personal, Policy Exchange. ---- Transportation The Economist (2012) “Watching Your Driving.”

Davenport, T. H. (2013) “At the Big Data Crossroads: turning towards a smarter travel

experience,” Amadeus IT Group. This report provides an overview of the benefits big data stands to bring to the travel industry. Seven case studies are considered, which underline the potential benefits in profits, customer relations, management, etc. Fried, Ina. “Volkswagen: Big Data Doesn’t Have to Mean Big Brother,” Recode, Mach 2014.

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12. General ICT & Economics Atkinson, R. D. and Stewart, L. A. (2013) “Just the Facts: The Economic Benefits of

Information and Communications Technology,” Information Technology and Innovation Foundation, May.

Borga, M. and Koncz-Bruner, J. (2012) Trends in Digitally Enabled Trade in Services, Bureau of

Economic Analysis Brynjolfsson, E. et al. (2008) “Scale without Mass,” Harvard Business School Technology &

Operations Mgt. Unit Research Paper No. 07-016 Bughin, J. et al. (2011) “The Impact of Internet technologies: Search,” McKinsey & Company,

July. Bughin, J., Byers, A. H. Chui, M. (2011) “How social technologies are extending the

organization,” The McKinsey Quarterly, November. Cisco (2014) Visual Networking Index Forecast Highlights. Deighton, J. and Kornfeld, L. (2012) Economic Value of the Advertising-Supported Internet

Ecosystem, Interactive Advertising Bureau. Ezell, S. (2012) Boosting Exports, Jobs and Economic Growth by Expanding the ITA,

Information Technology and Innovation Foundation, March. Jensen, J. B. (2011) Global Trade in Services: Fear, Facts and Offshoring, Peterson Institute for

International Economics, August. Lund, D. et al. (2014) Worldwide and Regional Internet of Things (IoT) 2014–2020 Forecast: A

Virtuous Circle of Proven Value and Demand, IDC Report, May. OECD (2012) Internet Economy Outlook. ---- (2011) The Future of the Internet Economy: A Statistical Profile, June. ---- (2012) The Impact of Internet in OECD Economies. Shirky, C. (2010) Cognitive Surplus: Creativity and generosity in a connected age (New

York: Penguin Press. Tapscott, D. and Williams, A. D. (2006) Wikinomics: How Mass collaboration Changes

Everything, New York: Penguin. UNCTAD (2012) Information Economy Report. ---- (2010) Creative Economy Report.

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13. General ICT & Employment Atkinson, R. and Miller, B. (2013) “Are Robots Taking Our Jobs, or Making Them?”, The

Information Technology & Innovation Foundation September.

This piece argues that rising productivity and automation thanks to smart technologies will not lead to lower employment. The “neo-Luddite” narrative wrongly assumes that there is a limited amount of labor while ignoring the savings generated by productivity, which spur more demand and create more jobs. According to the authors, because demand is infinite, there will always be labor demand, giving the lie to alarmist fears about long-term unemployment caused by productivity.

Autor, D. (2010) “The Polarization of Job Opportunities in the U.S. Labor Market: Implications

for Employment and Earnings,” Center for American Progress, the Hamilton Project.* The author argues that key challenges facing the U.S. labor market, predating the Great Recession, will endure. These are twofold: 1) the rise in US education levels has no kept up with the rising demand for skilled workers, resulting in a sharp rise in wage inequality; 2) The structure of job opportunities in the US has sharply polarized over the past two decades, with expanding job opportunities in both high-skill, high-wage, occupations and low-skill, low-wage occupations, coupled with contracting opportunities in middle-wage, middle-skill white-collar and blue-collar jobs. This study outlines and explores the “forces” shaping this trajectory in the US economy and the causes and consequences of these trends in US employment patterns. Autor, D. and Dorn, D. (2013) “The Growth of Low-Skill Service Jobs and the Polarization of

the US Labor Market,” American Economic Review 103(5).

The authors argue that the interaction of consumer preferences and falling cost of automation of job tasks leads to a polarization of both employment and wages. The authors consider and reject three alternative explanations for the polarization: i) increased offshoring; ii) rising income at the top of the wage distribution; iii) rising returns to skill. The authors conclude that while each has empirical grounding, none is sufficient as a leading explanation

Bughin, J. et al. (2011) “Internet matters: The Net’s Sweeping Impact on Growth

Jobs, and Prosperity,” McKinsey Global Institute, May. This study claims to be among the first “quantitative assessment[s] of the impact of the Internet on GDP and growth while also considering the most relevant tools government and businesses can use to get the most benefit from the Internet.” The authors provide an overview of the economic impact of the Internet, from e-commerce, communications, to traditional industries, with a view to estimating the value the Internet contributes to national economies. It is suggested that the Internet revolution is analogous to the development and commercialization of electric power over a century ago. ---- (2012) “The Social Economy: Unlocking the Value and Productivity

Through Social Technologies,” McKinsey Global Institute, July. This report chronicles the potential benefits and economic value to businesses who effectively implement “IT products and services that enable the formation and operation of online communities, where participants have distributed access to content and distributed rights to create, add, and/or modify content.” The report argues that these technologies can “disintermediate” commercial relationships and upend traditional business models, as well as boost the productivity of knowledge workers by 20 to 25%, bringing a value of more than $1 trillion annually to the economy overall. Also discussed are ways to unlock this value, which as of yet remains largely untapped.

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Cowen, T. (2011) The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better, Penguin. Ford, M. (2009) The Lights in the Tunnel: Automation, Accelerating Technology,

and the Economy of the Future. Acculant Publishing. Manyika, J., Remes, J. Roxburgh, C. (2011) “Why productivity can grow without killing jobs,”

The McKinsey Quarterly, p. 30, Spring. Orszag, P. (2011) “Hard Slog: the real future of the U.S. economy,” Bloomberg, July. Peck, D. (2010) “How A New Jobless Era Will Transform American,” The Atlantic. Rifkin, J. (1996) The End of Work: The Decline of the Global Labor Force and the

Dawn of the Post-Market Era, Tarcher.