A Tracing System for User Interactions towards Knowledge Extraction of Power Users in Business Intelligence Systems

Safwan Sulaiman, Tariq Mahmoud, Stephan Robbers, Jorge Marx Gómez, Joachim Kurzhöfer

Abstract

Business intelligence has been widely integrated in enterprises to help their employees in their decision making process by delivering the needed information at the right time. Statistics from Gartner Group showed that the investment in the business intelligence domain has recently been very high. However, different studies and market researches showed that the pervasiveness and the usage percentage rate of business intelligence are still very low. The reason behind that is the complexity of the usage of business intelligence systems. Moreover, enterprise users lack analytical skills. To mitigate this problem, a new concept of self-service business intelligence has been developed. Within this system, the knowhow of power user is extracted and delivered to business users in form of recommendations. In this paper, we present the conception and development of the tracing module of this new system. This module has the goal of tracing the interactions of power users as the first step to extract their procedural knowledge in form of analysis paths. This is done by creating a user interaction catalogue in which the interactions are defined based on their relevance to the knowledge extraction process. Finally, this paper presents the internal architecture of this tracing module and its components.

References

  1. Bange, C. (2016), Werkzeuge für analytische Informationssysteme, in Gluchowski, P. and Chamoni, P. (Eds.), Analytische Informationssysteme, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 97-126.
  2. BARC Research (2016), The BI Survey 15, available at: http://barc-research.com/bi-survey-15/ (accessed 28 April 2016).
  3. Bijker, M. and Hart, M. (2013), Factors Influencing Pervasiveness of Organisational Business Intelligence.
  4. Boyer, J., Frank, B., Green, B., Harris, T. and van de Vanter, K. (2010), Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence, MC Press, LLC.
  5. Evelson, B. (2013), Top 10 BI Predictions for 2013 and Beyond, available at: http://blogs.forrester.com/ boris_evelson/12-12-12-top_10_bi_predictions_for_20 13_and_beyond (accessed 6 April 2016).
  6. Gartner (2015), Flipping to Digital Leadership, Insights from the 2015 Gartner CIO Agenda Report.
  7. Gartner (2016), Gartner Says Worldwide Business Intelligence and Analytics Market to Reach $16.9 Billion in 2016”, available at: http://www.gartner. com/newsroom/id/3198917.
  8. Gioia, A., Cazzin, G. and Damiani, E. (2008), SpagoBI: A distinctive approach in open source business intelligence, Phitsanuloke, Thailand.
  9. Gluchowski, P. and Chamoni, P. (2016), Analytische Informationssysteme, Springer Berlin Heidelberg, Berlin, Heidelberg.
  10. Haneke, U., Trahasch, S. and Al., T.H.e. (2010), Open Source Business Intelligence (OSBI): Möglichkeiten, Chancen und Risiken quelloffener BI-Lösungen, 1. Aufl., Carl Hanser Fachbuchverlag, [S.l.].
  11. Hart, M., Esat, F., Rocha, M. and Khatieb, Z. (2007), Introducing students to business intelligence: acceptance and perceptions of OLAP software, Informing Science: International Journal of an Emerging Transdiscipline, Vol. 4 No. 1, pp. 105-123.
  12. Hawking, P. and Sellitto, C. (2015), Business Intelligence Strategy: A Utilities Company Case Study, Int. J. Enterp. Inf. Syst., Vol. 11 No. 1, pp. 1-12.
  13. Imhoff, C. and White, C. (2011), Self-Service Business Intelligence, TDWI Best Practices Report, Third Quarter.
  14. Infor (2013), The democratization of data. How information can give power to your people, available at: http://www.infor.com/content/whitepapers/democratiz ation-of-data.pdf (accessed 6 April 2016).
  15. Kemper, H.-G., Baars, H. and Mehanna, W. (2010), Business Intelligence - Grundlagen und praktische Anwendungen, Vieweg+Teubner, Wiesbaden.
  16. McLean, V. (2015), Investment in Self-Service Analytics to Increase, Finds 2015 State of Self-Service BI Report, available at: https://www.logianalytics.com/ news/investment-in-self-service-analytics-to-increasefinds-2015-state-of-self-service-bi-report/ (accessed 9 April 2016).
  17. Mertens, M. (2013), KNOBI - knowledge-based business intelligence for business user information-self-service, Oldenburg computer science series, Vol. 26, OlWIR, Oldenburger Verl. für Wirtschaft, Informatik und Recht, Edewecht.
  18. Morton, M.S.S. (1983), State of the art of research in Management Support Systems, Center for Information Systems Research, Sloan School of Management, Massachusetts Institute of Technology.
  19. Peters, D. (2014), Adaptive Lehr- und Lernsysteme zur Unterstützung der praktischen Ausbildung an ERPSystemen, Oldenburger Schriften zur Wirtschaftsinformatik, Bd. 13, Shaker, Aachen.
  20. Power, D.J. (2008), Decision Support Systems: A Historical Overview, in Burstein, F. and W. Holsapple, C. (Eds.), Handbook on Decision Support Systems 1, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 121-140.
  21. Sulaiman, S., Mahmoud, T., Gómez, J.M. and Kurzhöfer, J. (2015), Automatic Knowledge Transfer-based Architecture towards Self-Service Business Intelligence, Rome, Italy.
  22. T. E. Yoon, B. Ghosh and Bong-Keun Jeong (2014), User Acceptance of Business Intelligence (BI) Application: Technology, Individual Difference, Social Influence, and Situational Constraints.
  23. Waisberg, D. and Kaushik, A. (2009), Web Analytics 2.0: empowering customer centricity, The original Search Engine Marketing Journal, Vol. 2 No. 1, pp. 5-11.
  24. Wixom, B. and Watson, H. (2012), The BI-Based Organization, in Herschel, R.T. (Ed.), Organizational Applications of Business Intelligence Management, IGI Global, pp. 193-208.
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Paper Citation


in Harvard Style

Sulaiman S., Mahmoud T., Robbers S., Marx Gómez J. and Kurzhöfer J. (2016). A Tracing System for User Interactions towards Knowledge Extraction of Power Users in Business Intelligence Systems . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016) ISBN 978-989-758-203-5, pages 199-207. DOI: 10.5220/0006053601990207


in Bibtex Style

@conference{kmis16,
author={Safwan Sulaiman and Tariq Mahmoud and Stephan Robbers and Jorge Marx Gómez and Joachim Kurzhöfer},
title={A Tracing System for User Interactions towards Knowledge Extraction of Power Users in Business Intelligence Systems},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016)},
year={2016},
pages={199-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006053601990207},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS, (IC3K 2016)
TI - A Tracing System for User Interactions towards Knowledge Extraction of Power Users in Business Intelligence Systems
SN - 978-989-758-203-5
AU - Sulaiman S.
AU - Mahmoud T.
AU - Robbers S.
AU - Marx Gómez J.
AU - Kurzhöfer J.
PY - 2016
SP - 199
EP - 207
DO - 10.5220/0006053601990207