Nowadays, there is an approved concept called Business Intelligence that supports the decision making process. By extending Business Intelligence, a new concept called Adaptive Business Intelligence has been emerged. The current state in Adaptive Business Intelligence (ABI) is that decisions are not evaluated in a periodic manner and the inappropriate decisions of the past might occur again. The enhancement of decision quality is one of the major outputs behind this article. The evaluation of past decisions makes it helpful to take future complex decisions based on the uncertainty or confusion of historical decisions. The adaptability behind the proposed solution is achieved through the evaluation, tracking and recommendation of decisions in any Business Intelligence system. This article presents a reference architecture for a new approach called KPI-based decision impact evaluation system for adaptive business intelligence that can enrich the ABI applications.
business intelligence, adaptive business intelligence, decisions, evaluation
Burmester L. (2011). Adaptive Business-Intelligence-Systeme. Springer. Codd, E. F., S. B. Codd, and C. T. Davenport T. H., Harris J. G. De Long D. W., and Jacobson A. L. (2001). Data to knowledge to results: Building an analytic capability. California Management Review, vol. 43, n° 2.
Dillman D., Smyth J., Christian L. (2009). Internet, Mail, and Mixed. Mode Surveys: The Tailored Design Method. 3rd Edition. Hoboken, NJ: John Wiley & Sons, Inc. Duarte F. (2010). Working with Corporate Social Responsibility in Brazilian companies: the role of managers’ values in the maintenance of CSR cultures, Journal of Business Ethics, vol. 96, n° 3, p. 355-368. http://dx.doi.org/10.1007/s10551-010-0470-9
Etzioni A. (1988). The moral dimension: Toward a new economics, Cerca con Google, New York: Free Press., 150.
Fabac R. (2010). Complexity in organizations and environment-adaptive changes and adaptive decision-making. Interdisciplinary Description of Complex Systems, vol. 8, n° 1, p. 34-48.
Gluchowski P., Gabriel R., Chamoni P. (2005). Management Support Systeme und Business Intelligence: Computergestützte Informationssystemefür Fach-und Führungskräfte. Springer-Verlag New York, Inc.
Gómez J. M., Rautenstrauch C., Cissek P. (2008). Einführung in Business Intelligence mit SAP NetWeaver 7.0. Springer.
Jörg T. and S. Dessloch (2010). Near real-time data warehousing using state-of-the-art ETL tools. In Enabling Real-Time Business Intelligence, p. 100-117. Springer.
Karlsson R. (2013). Data as intelligence: A study of business intelligence as decision support. Kemper H.-G., Mehanna W., Unger C. (2006). Business Intelligence - Grundlagen Und PraktischeAnwendungen: EineEinführung in Die It-BasierteManagementunterstützung.
Springer DE. Kim J., Hwang M., Jeong D.-H., Song S.-K., Jung H. (2013). Business Intelligence Service
based on adaptive user modeling and groups. Journal of Computer Science, vol. 9, n° 10, 1396.
Kimball R. (1996). The Data Warehouse Toolkit. Wiley. ISBN 978-0-471-15337-5.
Laney D. (2001). 3-D Data Management: Controlling Data Volume, Velocity and Variety. META Group Research Note, February, 6.
Lau R. Y., S. S. Liao, K.-F. Wong, and D. K. Chiu (2012). Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions. MIS Quarterly, vol. 36, n° 4.
March J. G. (1987). Ambiguity and accounting: The elusive link between information and decision making. Accounting. Organizations and Society, vol. 12, n° 2, p. 153-168.
Marin-Ortega P. M., Dmitriyev V., Abilov M., Marx Gomez J. (2014). ELTA: New Approach in Designing Business Intelligence Solutions in Era of Big Data. Procedia Technology, vol. 16, n° 0, p. 667-674. doi:http://dx.doi.org/10.1016/j.protcy.2014.10.015
Michalewicz Z., Schmidt M., Michalewicz M., Chiriac C. (2006). Adaptive business intelligence. Springer.
Nenortaite J., Butleris R. (2009). Improving business rules management through the application of adaptive business intelligence technique. Information Technology and Control, vol. 38, n° 1, 21-28.
Oreizy P., Heimbigner D., Johnson G., Gorlick M. M., Taylor R. N., Wolf A. L., Medvidovic N., Rosenblum D. S., and Quilici A. (1999). An architecture-based approach to selfadaptive software. IEEE Intelligent systems, vol. 14, n° 3, p. 54-62.
Rezaie K., Ansarinejad A., Haeri A., Nazari-Shirkouhi A., Nazari-Shirkouhi S. (2011). Evaluating the business intelligence systems performance criteria using group fuzzy ahp approach. In Computer Modelling and Simulation (UKSim), UkSim 13th International Conference on, p. 360-364. IEEE.
Rezgui A. and Naana M. (2010). Improving of environmental management accounting system for support the environmental information management. Shaker Verlag.
Rosce J. T. (1975). Fundamental research statistics for the behavioural sciences. 2nd ed. New
York, NY, USA: Holt Rinehart & Winston.Salley (1993). Providing OLAP (on-line analytical processing) to user-analysts: An IT mandate. Codd and Date 32.
Simon H. A. (1977). The logic of heuristic decision making. In Models of discovery, Springer, p. 154-175
Turban E., Sharda R., Delen D. (2011). Decision support and business intelligence systems. Pearson Education India.
Villegas Machado N. M., Müller H. A., Tamura Morimitsu G. (2011). On designing selfadaptive software systems. Sistemas y Telemática.
Ward J. S., Barker A. (2013). Undefined By Data: A Survey of Big Data Definitions. arXiv preprint arXiv:1309.5821.
Zachman J. (2014, November). The Zachman Framework: The Official Concise Definition. Retrieved from http://www.zachmaninternational.com/index.php/the-zachman-framework
Zicker J. (1998). Real-time data warehousing. DM Review, March.