A formal definition of Big Data based on its essential features

Library Review

Volumn 65, Issue 3, 2016, Pages 122-135

  De Mauro, Andrea ^a   Greco, Marco ^b   Grimaldi, Michele ^b  

^a UNIVERSITY OF ROME TOR VERGATA   (Italy)

^b UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

Author keywords

Digital libraries; Information science; Information systems; Information technology; Knowledge management

Indexed keywords

EID: 84961566695     PISSN: 00242535     EISSN: None     Source Type: Journal    
DOI: 10.1108/LR-06-2015-0061     Document Type: Review

References (53)

1. Anderson, C. (2007), “The end of theory: the data deluge makes the scientific method obsolete”, Wired, Vol. 16 No. 7, available at: www.wired.com/2008/06/pb-theory/ (accessed 27 January 2016).
2. Askitas, N. and Zimmermann, K.F. (2009), “Google econometrics and unemployment forecasting”, Applied Economics Quarterly, Vol. 55 No. 2, pp. 107-120, available at: http://doi.org/10.3790/aeq.55.2.107
3. Atzori, L., Iera, A. and Morabito, G. (2010), “The internet of things: a survey”, Computer Networks, Vol. 54 No. 15, pp. 2787-2805.
4. Beyer, M.A. and Laney, D. (2012), The Importance of “Big Data”: A Definition, Gartner, Stamford, CT.
5. Boyd, D. and Crawford, K. (2012), “Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon”, Information, Communication & Society, Vol. 15 No. 5, pp. 662-679, available at: http://doi.org/10.1080/1369118X.2012.678878
6. Buhl, H.U., Röglinger, M., Moser, F. and Heidemann, J. (2013), “Big data”, Business & Information Systems Engineering, Vol. 5 No. 2, pp. 65-69, available at: http://doi.org/10.1007/s12599-013-0249-5
7. Candela, L., Castelli, D., Ferro, N., Ioannidis, Y., Koutrika, G., Meghini, C. and Schuldt, H. (2007), “The DELOS digital library reference model”, Foundations for Digital Libraries.
8. Candela, L., Manghi, P. and Ioannidis, Y. (2012), “Fourth workshop on very large digital libraries: on the marriage between very large digital libraries and very large data archives”, ACM SIGMOD Record, Vol. 40 No. 4, pp. 61-64, available at: http://doi.org/10.1145/2094114.2094130
9. Chen, H., Chiang, R. and Storey, V. (2012), “Business intelligence and analytics: from big data to big impact”, MIS Quarterly, Vol. 36 No. 4, pp. 1165-1188.
10. Coyle, K. (2006), “Mass digitization of books”, Journal of Academic Librarianship, Vol. 32 No. 6, pp. 641-645.
11. Cricelli, L. and Grimaldi, M. (2008), “A dynamic view of knowledge and information: a stock and flow based methodology”, International Journal of Management and Decision Making, Vol. 9 No. 6, pp. 686-698, available at: http://doi.org/10.1504/IJMDM.2008.021221
12. Davenport, T.H. and Patil, D.J. (2012), “Data scientist: the sexiest job of the 21st century”, Harvard Business Review, Vol. 90 No. 10, pp. 70-76.
13. De Mauro, A., Greco, M. and Grimaldi, M. (2015), “What is big data? A consensual definition and a review of key research topics”, International Conference on Integrated Information (IC-ININFO 2014) AIP Conference Proceedings, Madrid, Vol. 1644, pp. 97-104, AIP Publishing LLC, Madrid, available at: http://doi.org/10.1063/1.4907823
14. Dean, J. and Ghemawat, S. (2008), “MapReduce: simplified data processing on large clusters”, Communications of the ACM, Vol. 51 No. 1, pp. 1-13.
15. Dijcks, J. (2013), “Oracle: Big data for the enterprise”, Oracle White Paper, Oracle Corporation, Redwood Shores, CA.
16. Dumbill, E. (2013), “Making sense of big data”, Big Data, Vol. 1 No. 1, pp. 1-2.
17. Estrin, D., Culler, D., Pister, K. and Sukhatme, G. (2002), “Connecting the physical world with pervasive networks”, IEEE Pervasive Computing, Vol. 1 No. 1, pp. 59-69, available at: http://doi.org/10.1109/MPRV.2002.993145
18. Evans, D. (2011), The Internet of Things – How the Next Evolution of the Internet is Changing Everything, Cisco Systems, San Jose, CA.
19. Fisher, D., DeLine, R., Czerwinski, M. and Drucker, S. (2012), “Interactions with big data analytics”, Interactions, available at: http://dx.doi.org/10.,1145/2168931.2168943
20. Gartner (2014), “Gartner says the internet of things will transform the data center”, available at: www.gartner.com/newsroom/id/2684616
21. Ghemawat, S., Gobioff, H. and Leung, S.T. (2003), “The Google file system”, ACM SIGOPS Operating Systems Review, Vol. 37 No. 5, pp. 29-43.
22. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S. and Brilliant, L. (2009), “Detecting influenza epidemics using search engine query data”, Nature, Vol. 457 No. 7232, pp. 1012-1014, available at: http://doi.org/10.1038/nature07634
23. Google (2015), “Google Books library project – an enhanced card catalog of the world’s books”, available at: www.google.com/googlebooks/library (accessed July 20 2015).
24. Guzman, G. (2011), “Internet search behavior as an economic forecasting tool: the case of inflation expectations”, Journal of Economic and Social Measurement, Vol. 36 No. 3, pp. 119-167.
25. Hilbert, M. and López, P. (2011), “The world’s technological capacity to store, communicate, and compute information”, Science, Vol. 332 No. 6025, pp. 60-65, available at: http://doi.org/10.1126/science.1200970
26. Intel IT Center (2012), Big Data Analytics: Intel’s IT Manager Survey on How Organizations Are Using Big Data, Intel IT Center, Intel Corporation, Santa Clara, CA.
27. Jansen, W., Barbera, R., Drescher, M., Fresa, A., Hemmje, M., Ioannidis, Y. and Stanchev, P. (2013), “e-infrastructures for digital libraries…the future”, Lecture Notes in Computer Science, Vol. 8092, pp. 480-481, available at: http://doi.org/10.1007/978-3-642-40501-3
28. Laney, D. (2001), “3-D data management:controlling data volume, velocity and variety”, META Group Research Note, February, pp. 1-4.
29. McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D. and Barton, D. (2012), “Big data: the management revolution”, Harvard Business Review, Vol. 90 No. 10, pp. 61-67.
30. Manovich, L. (2012), “Trending: the promises and the challenges of big social data”, in Gold, M.K. (Ed.), Debates in the Digital Humanities, University of Minnesota Press, Minneapolis, MN, pp. 460-475.
31. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A.H. (2011), Big Data: The Next Frontier for Innovation, Competition, and Productivity, McKinsey Global Institute, New York, NY.
32. Mayer-Schönberger, V. and Cukier, K. (2013), Big Data: A Revolution That Will Transform How We Live, Work and Think, John Murray, London.
33. Michel, J.B., Shen, Y.K., Aiden, A.P., Veres, A., Gray, M.K., Pickett, J.P. and Aiden, E.L. (2011), “Quantitative analysis of culture using millions of digitized books”, Science, Vol. 331 No. 6014, pp. 176-182, available at: http://doi.org/10.1126/science.1199644
34. Michelino, F., Bianco, F. and Caputo, M. (2008), “Internet and supply chain management: adoption modalities for Italian firms”, Management Research News, Vol. 31 No. 5, pp. 359-374.
35. Microsoft Research (2013), “The big bang: how the big data explosion is changing the world”, available at: www.microsoft.com/enterprise/it-trends/big-data/articles/the-big-bang-how-the-big-data-explosion-is-changing-the-world.aspx#fbid=mH4SjMUrH9Y
36. Moore, G.E. (2006), “Cramming more components onto integrated circuits, reprinted from Electronics”, Vol. 38 No. 8, April 19, 1965, pp. 114, ff, IEEE Solid-State Circuits Newsletter, Vol. 11 No. 5, pp. 33-35, available at: http://doi.org/10.1109/N-SSC.2006.4785860
37. NBD-PWG NIST (2014), “NIST big data public working group”, Draft of Big Data Definition.
38. Pearson, T. and Wegener, R. (2013), Big Data: The Organizational Challenge, Bain & Company, available at: www.bain.com/images/bain_brief_big_data_the_organizational_challenge.pdf.
39. Powell, J. (2012), “‘At scale’ author name matching with Hadoop/MapReduce”, Library Hi Tech News, Vol. 29 No. 4, pp. 6-12, available at: http://doi.org/10.1108/07419051211249455
40. Prescott, A. (2013), “Bibliographic records as humanities big data”, Proceedings – 2013 IEEE International Conference on Big Data, IEEE, pp. 55-58, available at: http://doi.org/10.1109/BigData.,2013.6691670
41. Ronda-Pupo, G.A. and Guerras-Martin, L.A. (2012), “Dynamics of the evolution of the strategy concept 1962-2008: A co-word analysis”, Strategic Management Journal, Vol. 33 No. 2, pp. 162-188, available at: http://doi.org/10.1002/smj.948
42. Rowley, J. (2007), “The wisdom hierarchy: representations of the DIKW hierarchy”, Journal of Information Science, Vol. 33 No. 2, pp. 163-180.
43. Russom, P. (2011), “Big data analytics”, TDWI Best Practices Report, Fourth Quarter, pp. 1-35, available at: www.tableau.com/sites/default/files/whitepapers/tdwi_bpreport_q411_big_data_analytics_tableau.pdf
44. Schroeck, M., Shockley, R., Smart, J., Romero-Morales, D. and Tufano, P. (2012), Analytics: The Real-World Use of Big Data, IBM Institute for Business Value, Said Business School, New York, NY.
45. Shneiderman, B. (2008), “Extreme visualization: squeezing a billion records into a million pixels”, Proceedings of the 2008 ACM SIGMOD International Conference on Management of data, pp. 3-12, available at: http://doi.org/10.,1145/1376616.1376618
46. Shvachko, K., Kuang, H., Radia, S. and Chansler, R. (2010), “The Hadoop distributed file system”, IEEE 26th Symposium on Mass Storage Systems and Technologies, MSST2010, IEEE, pp. 1-10.
47. Suthaharan, S. (2014), “Big data classification: problems and challenges in network intrusion prediction with machine learning”, ACM SIGMETRICS Performance Evaluation Review, available at: http://doi.org/10.,1145/2627534.2627557
48. Viegas, F.B., Wattenberg, M., Van Ham, F., Kriss, J. and McKeon, M. (2007), “Many eyes: a site for visualization at internet scale”, IEEE Transactions on Visualization and Computer Graphics, Vol. 13 No. 6, pp. 1121-1128.
49. Wang, J., Zhang, Y., Gao, Y. and Xing, C. (2013), ” A new plug-in system supporting very large digital library”, in Urs, S.R., Na, J.C. and Buchanan, G. (Eds), 15th International Conference on Asia-Pacific Digital Libraries, ICADL 2013, Bangalore, 9-11 December, (Lecture No, Vol. 8279, pp. 45-52), Springer International Publishing, Bangalore, available at: http://doi.org/10.1007/978-3-319-03599-4
50. Ward, J.S. and Barker, A. (2013), “Undefined by data: a survey of big data definitions”, available at: arXiv:,1309.5821[cs.DB]
51. Xiong, W., Yu, Z., Bei, Z., Zhao, J., Zhang, F., Zou, Y. and Xu, C. (2013), “A characterization of big data benchmarks”, Proceedings – 2013 IEEE International Conference on Big Data, Big Data 2013, pp. 118-125, available at: http://doi.org/10.1109/BigData.,2013.6691707
52. Zaslavsky, A., Perera, C. and Georgakopoulos, D. (2013), “Sensing as a service and big data”, available at: arXiv:,1301.0159[cs.CY]
53. Zikopoulos, P. and Eaton, C. (2011), Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill Osborne Media, New York, NY.