Abstract
Big data analytics requires technologies to efficiently process large quantities of data. Moreover, especially in decision making, it not only requires individual intellectual capabilities in the analytical activities but also collective knowledge. Very often, people with diverse expert knowledge need to work together towards a meaningful interpretation of the associated results for new insight. Thus, a big data analysis infrastructure must both support technical innovation and effectively accommodate input from multiple human experts. In this chapter, we aim to advance our understanding on the synergy between human and machine intelligence in tackling big data analysis. Sensemaking models for big data analysis were explored and used to inform the development of a generic conceptual architecture as a means to frame the requirements of such an analysis and to position the role of both technology and human in this synergetic relationship. Two contrasting real-world use case studies were undertaken to test the applicability of the proposed architecture for the development of a supporting platform for big data analysis. Reflection on this outcome has further advanced our understanding on the complexity and the potential of individual and collaborative sensemaking models for big data analytics.
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References
Agrawal, D., et al.: Challenges and opportunities with big data. Proc. VLDB Endow. 5(12), 2032–2033 (2012)
Barton, D., Court, D.: Making advanced analytics work for you. Harv Bus. Rev. 90(10), 78–83 (2012)
de la Calle, G, Alonso-Martínez, E., Tzagarakis, M., Karacapilidis, N.: The dicode workbench: a flexible framework for the integration of information and web services. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services (iiWAS2012), Bali, Indonesia, 3–5 Dec 2012, pp. 15–25 (2012)
Dervin, B.: From the mind’s eye of the user: the sense-making qualitative-quantitative methodology. In: Dervin, B., Foreman-Wernet, L., Lauterbach, E. (eds.) Sense-Making Methodology Reader: Selected Writings of Brenda Dervin. Hampton Press Inc, Cresskill (2003)
Fisher, D., DeLine, R., Czerwinski, M., Drucker, S.: Interactions with big data analytics. Interactions 19(3), 50–59 (2012)
Gartner, Inc.: Pattern-based strategy: getting value from big data. Gartner Group press release, July 2011. http://www.gartner.com/it/page.jsp?id=1731916
Henschen, D.: Why all the hadoopla? Inf. Week 11(14), 11 (2011)
Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)
Karacapilidis, N.: Mastering data-intensive collaboration and decision making through a cloud infrastructure: the dicode EU project. EMBnet. J. 17(1), 3 (2011)
Karacapilidis, N., Rüping, S., Tzagarakis, M., Poigné, A., Christodoulou, S.: Building on the synergy of machine and human reasoning to tackle data-intensive collaboration and decision making. Intelligent Decision Technologies. Springer, Berlin, 2011, pp. 113–122
Kiron, D., Shockley, R., Kruschwitz, N., Finch, G., Haydock, M. Analytics: the widening divide. MIT Sloan Manage. Rev. 53(3), 1–20 (2011)
Kittur, A., Chau, D.H., Faloutsos, C., Hong, J.I.: Supporting Ad hoc sensemaking: integrating cognitive, hci, and data mining approaches. In: Sensemaking Workshop at CHI, Boston, MA (2009)
Klein, G., Moon, B., Hoffman, R.R.: Making sense of sensemaking 2: a macrocognitive model. Intel. Syst. IEEE 21(5), 88–92 (2006)
Lee, C.P., Abrams, S.: Group sensemaking. In: Position Paper for Workshop on Sensemaking. ACM Conference on Human Factors and Usability (CHI), Florence, Italy (2008)
Lim, E.-P., Chen, H., Chen, G.: Business intelligence and analytics: research directions. ACM Trans. Manage. Inf. Syst. (TMIS) 3(4), 17 (2013)
Lohr, S.: The age of big data. New York times, Feb 11, 2012 (2012). http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html
Maiden, N., Jones, S. Karlsen, K., Neill, R., Zachos, K., Milne, A.: Requirement engineering as creative problem solving: a research agenda for idea finding. In: Proceedings of 18th IEEE International Conference on Requirements Engineering (RE’10). IEEE Press, pp. 57–66 (2010)
March, S.T., Smith, G.F.: Design and natural science research on information technology. Decis. Support Syst. 15(4), 251–266 (1995)
Ntuen, C.A., Balogun, O., Boyle, E., Turner, A.: Supporting command and control training functions in the emergency management domain using cognitive systems engineering. Ergonomics 49(12–13), 1415–1436 (2006)
Paul, S.A., Reddy, M.C.: Understanding together: sensemaking in collaborative information seeking. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work. ACM (2010)
Peffers, K., Tuunanen, T., Rothenberegr, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. Manage. Inf. Syst. 24(3), 45–77 (2007)
Pirolli, P., Card, S.: The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In: Proceedings of International Conference on Intelligence Analysis, vol. 5 (2005)
Qu, Y., Furnas, G.: Model-driven formative evaluation of exploratory search: a study under a sensemaking framework. Inf. Process. Manage. 44(2), 534–555 (2008)
Qu, Y., Hansen, D.L.: Building shared understanding in collaborative sensemaking. In: Proceedings of CHI 2008 Sensemaking Workshop, 2008
Russell, D.M., Stefik, M.J., Pirolli, P., Card, S.K.: The cost structure of sensemaking. In: Proceedings of SIGCHI. ACM Press, New York, pp. 269–276 (1993)
Savolainen, R.: The sense-making theory: reviewing the interests of a user-centered approach to information seeking and use. Inf. Process. Manage. 29(1), 13–18 (1993)
Schoenfeld, A.H.: Learning to think mathematically: problem solving, metacognition, and sensemaking in mathematics, In: Grouws, D. (ed.) Handbook of Research on Mathematics Teaching and Learning. MacMillan, New York (1992)
Thakker, D., Dimitrova, V., Lau, L., Denaux, R., Karanasios S., Yang-Turner, F.: A priori ontology modularisation in ill-defined domains. In; Proceedings of the 7th International Conference on Semantic Systems. ACM (2011)
The Economist: Data, data everywhere. Feb 25, 2010. http://www.economist.com/node/15557443
Weick, K.E.: Sensemaking in Organizations. Sage Publications Inc, Thousand Oaks (1995)
Yang-Turner, F., Lau, L.: A pragmatic strategy for creative requirements elicitation: from current work practice to future work practice. In: Workshop on Requirements Engineering for Systems, Services and Systems-of-Systems (RESS), 2011. IEEE (2011)
Yang-Turner, F., Lau, L., Dimitrova, V.: A model-driven prototype evaluation to elicit requirements for a sensemaking support tool. In: Proceedings of the 2012 19th Asia-Pacific Software Engineering Conference, vol. 1. IEEE Computer Society (2012). doi: 10.1109/APSEC.2012.129
Yu, E.: Modelling strategic relationships for process reengineering. In: Yu, E., Giorgini, P., Maiden, N., Myopoulous, J. (eds.) Social Modeling for Requirements Engineering. The MIT Press, Cambridge (2011)
Zeng, L., Li, L., Duan, L.: Business intelligence in enterprise computing environment. Inf. Technol. Manage. 13(4), 297–310 (2012)
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Lau, L., Yang-Turner, F., Karacapilidis, N. (2014). Requirements for Big Data Analytics Supporting Decision Making: A Sensemaking Perspective. In: Karacapilidis, N. (eds) Mastering Data-Intensive Collaboration and Decision Making. Studies in Big Data, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-02612-1_3
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DOI: https://doi.org/10.1007/978-3-319-02612-1_3
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