Towards Interactive Data Processing and Analytics - Putting the Human in the Center of the Loop

Michael Behringer, Pascal Hirmer, Bernhard Mitschang

2017

Abstract

Today, it is increasingly important for companies to evaluate data and use the information contained. In practice, this is however a great challenge, especially for domain users that lack the necessary technical knowledge. However, analyses prefabricated by technical experts do not provide the necessary flexibility and are oftentimes only implemented by the IT department if there is sufficient demand. Concepts like Visual Analytics or Self-Service Business Intelligence involve the user in the analysis process and try to reduce the technical requirements. However, these approaches either only cover specific application areas or they do not consider the entire analysis process. In this paper, we present an extended Visual Analytics process, which puts the user at the center of the analysis. Based on a use case scenario, requirements for this process are determined and, later on, a possible application for this scenario is discussed that emphasizes the benefits of our approach.

References

  1. Alpar, P. and Schulz, M. (2016). Self-Service Business Intelligence. Business & Information Systems Engineering, 58(2):151-155.
  2. Bertini, E. and Lalanne, D. (2009). Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration, pages 12-20, New York, USA. ACM Press.
  3. Bögl, M., Aigner, W., Filzmoser, P., Lammarsch, T., Miksch, S., and Rind, A. (2013). Visual Analytics for Model Selection in Time Series Analysis. IEEE Transactions on Visualization and Computer Graphics, 19(12):2237-2246.
  4. Card, S. K., Mackinlay, J. D., and Shneiderman, B. (1999). Information Visualization. In Card, S. K., Mackinlay, J. D., and Shneiderman, B., editors, Readings In Information Visualization: Using Vision To Think, pages 1- 34. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  5. Chiticariu, L., Kolaitis, P. G., and Popa, L. (2008). Interactive generation of integrated schemas. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pages 833-846. ACM.
  6. Cypher, A., editor (1993). Watch What I Do - Programming by Demonstration. MIT Press, Cambridge, MA, USA.
  7. Daniel, F. and Matera, M. (2014). Mashups. Concepts, Models and Architectures. Springer, Berlin, Heidelberg.
  8. Eckerson, W. W. (2009). Self-Service BI. Checklist Report, TDWI Research.
  9. EMC Corporation (2014). Sensors. Press Release.
  10. Endert, A., Hossain, M. S., Ramakrishnan, N., North, C., Fiaux, P., and Andrews, C. (2014). The human is the loop: new directions for visual analytics. Journal of Intelligent Information Systems, 43(3):411-435.
  11. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). The kdd process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11):27-34.
  12. Gantz, J. and Reinsel, D. (2012). THE DIGITAL UNIVERSE IN 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. International Data Corporation (IDC).
  13. Hirmer, P. and Behringer, M. (2017). FlexMash 2.0 - Flexible Modeling and Execution of Data Mashups. In Daniel, F. and Gaedke, M., editors, Rapid Mashup Development Tools, pages 10-29. Springer International Publishing, Cham.
  14. Hirmer, P. and Mitschang, B. (2016). FlexMash - Flexible Data Mashups Based on Pattern-Based Model Transformation. In Daniel, F. and Pautasso, C., editors, Rapid Mashup Development Tools, pages 12-30. Springer International Publishing, Cham.
  15. Hirmer, P., Reimann, P., Wieland, M., and Mitschang, B. (2015). Extended Techniques for Flexible Modeling and Execution of Data Mashups. In Helfert, M., Holzinger, A., Belo, O., and Francalanci, C., editors, Proceedings of 4th International Conference on Data Management Technologies and Applications, pages 111-122. SciTePress.
  16. Imhoff, C. and White, C. (2011). Self-Service Business Intelligence. Best Practices Report, TDWI Research.
  17. Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N. H., Weaver, C., Lee, B., Brodbeck, D., and Buono, P. (2011a). Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization, 10(4):271-288.
  18. Kandel, S., Paepcke, A., Hellerstein, J., and Heer, J. (2011b). Wrangler: Interactive Visual Specification of Data Transformation Scripts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3363-3372. ACM, New York, NY, USA.
  19. Keim, D. A., Andrienko, G., Fekete, J.-D., G örg, C., Kohlhammer, J., and Melanc¸on, G. (2008). Visual Analytics: Definition, Process, and Challenges. In Kerren, A., Stasko, J. T., Fekete, J.-D., and North, C., editors, Information Visualization, pages 154-175. Springer, Berlin, Heidelberg.
  20. Keim, D. A., Kohlhammer, J., Mansmann, F., May, T., and Wanner, F. (2010). Visual Analytics. In Keim, D., Kohlhammer, J., Ellis, G., and Mansmann, F., editors, Mastering The Information Age, pages 7-18. Eurographics Association, Goslar.
  21. Keim, D. A., Mansmann, F., Schneidewind, J., and Ziegler, H. (2006). Challenges in Visual Data Analysis. In Proceedings of the International Conference on Information Visualisation, pages 9-16. IEEE.
  22. Kemper, H.-G., Baars, H., and Mehanna, W. (2010). Business Intelligence - Grundlagen und praktische Anwendungen. Eine Einführung in die IT-basierte Managementunterstützung. Vieweg+Teubner, Wiesbaden.
  23. Maimon, O. and Rokach, L. (2010). Introduction to Knowledge Discovery and Data Mining. In Maimon, O. and Rokach, L., editors, Data Mining and Knowledge Discovery Handbook. Springer, New York, Dordrecht, Heidelberg, London.
  24. Meunier, R. (1995). The pipes and filters architecture. In Coplien, J. O. and Schmidt, D. C., editors, Pattern Languages of Program Design, pages 427-440. ACM Press, New York, NY, USA.
  25. Pirolli, P. and Card, S. (2005). The Sensemaking Process and Leverage Points for Analyst Technology as Identified Through Cognitive Task Analysis. In Proceedings of the International Conference on Intelligence Analysis.
  26. Puolamäki, K., Bertone, A., Ther ón, R., Huisman, O., Johansson, J., Miksch, S., Papapetrou, P., and Rinzivillo, S. (2010). Data Mining. In Keim, D., Kohlhammer, J., Ellis, G., and Mansmann, F., editors, Mastering The Information Age, pages 39-56. Eurographics Association, Goslar.
  27. Raman, V. and Hellerstein, J. M. (2001). Potter's Wheel: An Interactive Data Cleaning System. In Proceedings of the International Conference on Very Large Data Bases (VLDB), pages 381-390.
  28. Sacha, D., Stoffel, A., Stoffel, F., Kwon, B. C., Ellis, G., and Keim, D. A. (2014). Knowledge Generation Model for Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 20(12):1604-1613.
  29. Shneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In Symposium on Visual Languages, pages 336-343. IEEE, Washington, DC, USA.
  30. Stodder, D. (2015). Visual Analytics for Making Smarter Decisions Faster. Best Practices Report, TDWI Research.
  31. Thomas, J. J. and Cook, K. A. (2005). Illuminating the Path: The Research and Development Agenda for Visual Analytics. National Visualization and Analytics Center.
Download


Paper Citation


in Harvard Style

Behringer M., Hirmer P. and Mitschang B. (2017). Towards Interactive Data Processing and Analytics - Putting the Human in the Center of the Loop . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-249-3, pages 87-96. DOI: 10.5220/0006326300870096


in Bibtex Style

@conference{iceis17,
author={Michael Behringer and Pascal Hirmer and Bernhard Mitschang},
title={Towards Interactive Data Processing and Analytics - Putting the Human in the Center of the Loop},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2017},
pages={87-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006326300870096},
isbn={978-989-758-249-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Towards Interactive Data Processing and Analytics - Putting the Human in the Center of the Loop
SN - 978-989-758-249-3
AU - Behringer M.
AU - Hirmer P.
AU - Mitschang B.
PY - 2017
SP - 87
EP - 96
DO - 10.5220/0006326300870096