Ontology-Driven Business Intelligence for Comparative Data Analysis

  • Thomas Neuböck
  • Bernd Neumayr
  • Michael Schrefl
  • Christoph Schütz
Chapter
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 172)

Abstract

In this tutorial, we present an ontology-driven business intelligence approach for comparative data analysis which has been developed in a joint research project, Semantic Cockpit (semCockpit), of academia, industry, and prospective users from public health insurers. In order to gain new insights into their businesses, companies perform comparative data analysis by detecting striking differences between different, yet similar, groups of data. These data groups consist of measure values which quantify real-world facts. Scores compare the measure values of different data groups. semCockpit employs techniques from knowledge-based systems, ontology engineering, and data warehousing in order to support business analysts in their analysis tasks. Concept definitions complement dimensions and facts by capturing relevant business terms which are used in the definition of measures and scores. Furthermore, domain ontologies serve as semantic dimensions and judgement rules externalize previous insights. Finally, we sketch a vision of analysis graphs and associated guidance rules to represent analysis processes.

Keywords

Business intelligence OLAP Data warehouses Semantic technologies 

Notes

Acknowledgments

This work is funded by the Austrian Ministry of Transport, Innovation, and Technology in program FIT-IT Semantic Systems and Services under grant FFG-829594 (Semantic Cockpit: an ontology-driven, interactive business intelligence tool for comparative data analysis).

References

  1. 1.
    Anderlik, S., Neumayr, B., Schrefl, M.: Using domain ontologies as semantic dimensions in data warehouses. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 88–101. Springer, Heidelberg (2012)Google Scholar
  2. 2.
    Bellatreche, L., Giacometti, A., Marcel, P., Mouloudi, H., Laurent, D.: A personalization framework for OLAP queries. In: Song, I.-Y., Trujillo, J. (eds.) DOLAP, pp. 9–18. ACM, New York (2005)Google Scholar
  3. 3.
    Bentayeb, F., Favre, C.: RoK: roll-up with the K-means clustering method for recommending olap queries. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2009. LNCS, vol. 5690, pp. 501–515. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    Berger, S., Schrefl, M.: Feddw: A tool for querying federations of data warehouses - architecture, use case and implementation. In: Cordeiro, J., Filipe, J. (eds.) ICEIS (1), pp. 113–122 (2009)Google Scholar
  5. 5.
    Buchheit, M., Nutt, W., Donini, F.M., Schaerf, A.: Refining the structure of terminological systems: Terminology = schema + views. In: Hayes-Roth, B., Korf, R.E. (eds.) AAAI, pp. 199–204. AAAI Press/The MIT Press (1994)Google Scholar
  6. 6.
    Calvanese, D., Giacomo, G.D., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Rosati, R., Ruzzi, M., Savo, D.F.: The mastro system for ontology-based data access. Semant. Web 2(1), 43–53 (2011)Google Scholar
  7. 7.
    Ceri, S., Brambilla, M., Fraternali, P.: The history of webML lessons learned from 10 years of model-driven development of web applications. In: Borgida, A.T., Chaudhri, V.K., Giorgini, P., Yu, E.S. (eds.) Conceptual Modeling: Foundations and Applications. LNCS, vol. 5600, pp. 273–292. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Das, S., Chong, E.I., Eadon, G., Srinivasan, J.: Supporting ontology-based semantic matching in rdbms. In: Nascimento, M.A., Özsu, M.T., Kossmann, D., Miller, R.J., Blakeley, J.A., Schiefer, K.B. (eds.) VLDB, pp. 1054–1065. Morgan Kaufmann (2004)Google Scholar
  9. 9.
    Fikes, R., Kehler, T.: The role of frame-based representation in reasoning. Commun. ACM 28(9), 904–920 (1985)CrossRefGoogle Scholar
  10. 10.
    Geerts, F., Kementsietsidis, A., Milano, D., et al.: \(i\)MONDRIAN: a visual tool to annotate and query scientific databases. In: Böhm, C. (ed.) EDBT 2006. LNCS, vol. 3896, pp. 1168–1171. Springer, Heidelberg (2006)Google Scholar
  11. 11.
    Giacometti, A., Marcel, P., Negre, E., Soulet, A.: Query recommendations for OLAP discovery driven analysis. In: Song, I.-Y., Zimányi, E. (eds.) DOLAP, pp. 81–88. ACM (2009)Google Scholar
  12. 12.
    Golfarelli, M., Maio, D., Rizzi, S.: The dimensional fact model: a conceptual model for data warehouses. Int. J. Coop. Inf. Syst. 7(2–3), 215–247 (1998)CrossRefGoogle Scholar
  13. 13.
    Golfarelli, M., Rizzi, S., Biondi, P.: myOLAP: an approach to express and evaluate OLAP preferences. IEEE Trans. Knowl. Data Eng. 23(7), 1050–1064 (2011)CrossRefGoogle Scholar
  14. 14.
    Heer, J., Mackinlay, J.D., Stolte, C., Agrawala, M.: Graphical histories for visualization: supporting analysis, communication, and evaluation. IEEE Trans. Vis. Comput. Graph. 14(6), 1189–1196 (2008)CrossRefGoogle Scholar
  15. 15.
    Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Commun. ACM 55(4), 45–54 (2012)CrossRefGoogle Scholar
  16. 16.
    Hitzler, P., Krötzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S. (eds.): OWL 2 Web Ontology Language: Primer. W3C Recommendation, 27 October 2009. http://www.w3.org/TR/owl2-primer/
  17. 17.
    Hürsch, W.L.: Should superclasses be abstract? In: Pareschi, R. (ed.) ECOOP 1994. LNCS, vol. 821, pp. 12–31. Springer, Heidelberg (1994)Google Scholar
  18. 18.
    Hurtado, C.A., Mendelzon, A.O.: Reasoning about summarizability in heterogeneous multidimensional schemas. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, p. 375. Springer, Heidelberg (2001)Google Scholar
  19. 19.
    Jerbi, H., Ravat, F., Teste, O., Zurfluh, G.: Preference-based recommendations for OLAP analysis. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 467–478. Springer, Heidelberg (2009)Google Scholar
  20. 20.
    Khouri, S., Bellatreche, L.: A methodology and tool for conceptual designing a data warehouse from ontology-based sources. In: Song, I.-Y., Ordonez, C. (eds.) DOLAP, pp. 19–24. ACM (2010)Google Scholar
  21. 21.
    Lehner, W., Albrecht, J., Wedekin, H.: Normal forms for multidimensional databases. In: Rafanelli, M., Jarke, M. (eds.) SSDBM, pp. 63–72. IEEE Computer Society (1998)Google Scholar
  22. 22.
    Lim, L., Wang, H., Wang, M.: Unifying data and domain knowledge using virtual views. In: Koch, C., Gehrke, J., Garofalakis, M.N., Srivastava, D., Aberer, K., Deshpande, A., Florescu, D., Chan, C.Y., Ganti, V., Kanne, C.-C., Klas, W., Neuhold, E.J. (eds.) VLDB, pp. 255–266. ACM (2007)Google Scholar
  23. 23.
    Malinowski, E., Zimányi, E.: Hierarchies in a multidimensional model: from conceptual modeling to logical representation. Data Knowl. Eng. 59(2), 348–377 (2006)CrossRefGoogle Scholar
  24. 24.
    Nebot, V., Llavori, R.B.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)CrossRefGoogle Scholar
  25. 25.
    Nebot, V., Berlanga, R., Pérez, J.M., Aramburu, M., Pedersen, T.B.: Multidimensional integrated ontologies: a framework for designing semantic data warehouses. In: Spaccapietra, S., Zimányi, E., Song, I.-Y. (eds.) Journal on Data Semantics XIII. LNCS, vol. 5530, pp. 1–36. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. 26.
    Neuböck, T., Neumayr, B., Rossgatterer, T., Anderlik, S., Schrefl, M.: Multi-dimensional navigation modeling using BI analysis graphs. In: Castano, S., Vassiliadis, P., Lakshmanan, L.V.S., Lee, M.L. (eds.) ER Workshops 2012. LNCS, vol. 7518, pp. 162–171. Springer, Heidelberg (2012)Google Scholar
  27. 27.
    Neumayr, B., Schrefl, M., Thalheim, B.: Hetero-homogeneous hierarchies in data warehouses. In: Link, S., Ghose, A. (eds.) APCCM. CRPIT, vol. 110, pp. 61–70. Australian Computer Society (2010)Google Scholar
  28. 28.
    Neumayr, B., Schütz, Ch., Schrefl, M.: Semantic enrichment of OLAP cubes: multi-dimensional ontologies and their representation in SQL and OWL. In: Meersman, R., Panetto, H., Dillon, T., Eder, J., Bellahsene, Z., Ritter, N., De Leenheer, P., Dou, D. (eds.) OTM 2013. LNCS, vol. 8185, pp. 624–641. Springer, Heidelberg (2013)Google Scholar
  29. 29.
    Niinimäki, M., Niemi, T.: An etl process for olap using rdf/owl ontologies. In: J. Data Semantics [39], pp. 97–119Google Scholar
  30. 30.
    Pardillo, J., Mazón, J.-N., Trujillo, J.: Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses. Inf. Sci. 180(5), 584–601 (2010)CrossRefGoogle Scholar
  31. 31.
    Romero, O., Abelló, A.: Automating multidimensional design from ontologies. In: Song, I.-Y., Pedersen, T.B. (eds.) DOLAP, pp 1–8. ACM (2007)Google Scholar
  32. 32.
    Romero, O., Abelló, A.: Open access semantic aware business intelligence. In: Zimányi, E. (ed.) eBISS 2013. LNCS, vol. 7911, pp. xx–yy. Springer, Heidelberg (2014)Google Scholar
  33. 33.
    Romero, O., Marcel, P., Abelló, A., Peralta, V., Bellatreche, L.: Describing analytical sessions using a multidimensional algebra. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 224–239. Springer, Heidelberg (2011)Google Scholar
  34. 34.
    Saggion, H., Funk, A., Maynard, D., Bontcheva, K.: Ontology-based information extraction for business intelligence. In: Aberer, K. (ed.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 843–856. Springer, Heidelberg (2007)Google Scholar
  35. 35.
    Sapia, C.: On modeling and predicting query behavior in OLAP systems. In: Gatziu, S., Jeusfeld, M.A., Staudt, M., Vassiliou, Y. (eds.) DMDW. CEUR Workshop Proceedings, vol. 19, pp. 1–10. CEUR-WS.org (1999)Google Scholar
  36. 36.
    Schrefl, M., Neumayr, B., Stumptner, M.: The decision-scope approach to specialization of business rules: Application in business process modeling and data warehousing. In: Proceedings of the Ninth Asia-Pacific Conference on Conceptual Modelling (APCCM 2013) (2013)Google Scholar
  37. 37.
    Silver, B.: BPMN Method and Style, 2nd edn., with BPMN Implementer’s Guide: A Structured Approach for Business Process Modeling and Implementation Using BPMN 2.0. Cody-Cassidy Press (2011)Google Scholar
  38. 38.
    Skoutas, D., Simitsis, A., Sellis, T.K.: Ontology-driven conceptual design of etl processes using graph transformations. In: J. Data Semantics [39], pp. 120–146Google Scholar
  39. 39.
    Spaccapietra, S., Zimányi, E., Song, I.-Y. (eds.): Journal on Data Semantics XIII. LNCS, vol. 5530. Springer, Heidelberg (2009)Google Scholar
  40. 40.
    Spahn, M., Kleb, J., Grimm, S., Scheidl, S.: Supporting business intelligence by providing ontology-based end-user information self-service. In: Duke, A., Hepp, M., Bontcheva, K., Vilain, M.B. (eds) OBI. ACM International Conference Proceeding Series, vol. 308, p. 10. ACM (2008)Google Scholar
  41. 41.
    Staudt, M., Jarke, M., Jeusfeld, M.A., Nissen, H.W.: Query classes. In: DOOD, pp. 283–295 (1993)Google Scholar
  42. 42.
    Thalhammer, T., Schrefl, M., Mohania, M.K.: Active data warehouses: complementing OLAP with analysis rules. Data Knowl. Eng. 39(3), 241–269 (2001)CrossRefGoogle Scholar
  43. 43.
    Thollot, R.: Dynamic Situation Monitoring and Context-Aware BI Recommendations. PhD thesis, Ecole Centrale Paris (2012)Google Scholar
  44. 44.
    Trujillo, J., Gómez, J., Palomar, M.S.: Modeling the behavior of OLAP applications using an UML compliant approach. In: Yakhno, T. (ed.) ADVIS 2000. LNCS, vol. 1909, pp. 14–23. Springer, Heidelberg (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Neuböck
    • 1
  • Bernd Neumayr
    • 2
  • Michael Schrefl
    • 2
  • Christoph Schütz
    • 2
  1. 1.Solvistas GmbHLinzAustria
  2. 2.Johannes Kepler University LinzLinzAustria

Personalised recommendations