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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
Systematized Nomenclature Of Medicine Clinical Terms.
- 4.
- 5.
In this and subsequent figures we omit for brevity the representation of “all”-nodes of points in dimension space DrugPrescription.
- 6.
- 7.
In this and subsequent figures we omit for brevity the representation of “top”-levels of a granularity in dimension space DrugPrescriptionSpace.
References
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)
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)
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)
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)
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)
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)
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)
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)
Fikes, R., Kehler, T.: The role of frame-based representation in reasoning. Commun. ACM 28(9), 904–920 (1985)
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)
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)
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)
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)
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)
Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Commun. ACM 55(4), 45–54 (2012)
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/
Hürsch, W.L.: Should superclasses be abstract? In: Pareschi, R. (ed.) ECOOP 1994. LNCS, vol. 821, pp. 12–31. Springer, Heidelberg (1994)
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)
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)
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)
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)
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)
Malinowski, E., Zimányi, E.: Hierarchies in a multidimensional model: from conceptual modeling to logical representation. Data Knowl. Eng. 59(2), 348–377 (2006)
Nebot, V., Llavori, R.B.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)
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)
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)
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)
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)
Niinimäki, M., Niemi, T.: An etl process for olap using rdf/owl ontologies. In: J. Data Semantics [39], pp. 97–119
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)
Romero, O., Abelló, A.: Automating multidimensional design from ontologies. In: Song, I.-Y., Pedersen, T.B. (eds.) DOLAP, pp 1–8. ACM (2007)
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)
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)
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)
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)
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)
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)
Skoutas, D., Simitsis, A., Sellis, T.K.: Ontology-driven conceptual design of etl processes using graph transformations. In: J. Data Semantics [39], pp. 120–146
Spaccapietra, S., Zimányi, E., Song, I.-Y. (eds.): Journal on Data Semantics XIII. LNCS, vol. 5530. Springer, Heidelberg (2009)
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)
Staudt, M., Jarke, M., Jeusfeld, M.A., Nissen, H.W.: Query classes. In: DOOD, pp. 283–295 (1993)
Thalhammer, T., Schrefl, M., Mohania, M.K.: Active data warehouses: complementing OLAP with analysis rules. Data Knowl. Eng. 39(3), 241–269 (2001)
Thollot, R.: Dynamic Situation Monitoring and Context-Aware BI Recommendations. PhD thesis, Ecole Centrale Paris (2012)
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)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Neuböck, T., Neumayr, B., Schrefl, M., Schütz, C. (2014). Ontology-Driven Business Intelligence for Comparative Data Analysis. In: Zimányi, E. (eds) Business Intelligence. eBISS 2013. Lecture Notes in Business Information Processing, vol 172. Springer, Cham. https://doi.org/10.1007/978-3-319-05461-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-05461-2_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05460-5
Online ISBN: 978-3-319-05461-2
eBook Packages: Computer ScienceComputer Science (R0)