Skip to main content
SpringerLink
Log in
Book cover

European Big Data Management and Analytics Summer School

eBISS 2013: Business Intelligence pp 77–120Cite as

Ontology-Driven Business Intelligence for Comparative Data Analysis

  • Chapter

Part of the book series: Lecture Notes in Business Information Processing ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    http://www.tableausoftware.com

  2. 2.

    http://www.oracle.com

  3. 3.

    Systematized Nomenclature Of Medicine Clinical Terms.

  4. 4.

    http://www.w3.org/TR/owl2-syntax/#Global_Restrictions_on_Axioms_in_OWL_2_DL

  5. 5.

    In this and subsequent figures we omit for brevity the representation of “all”-nodes of points in dimension space DrugPrescription.

  6. 6.

    http://www.tableausoftware.com

  7. 7.

    In this and subsequent figures we omit for brevity the representation of “top”-levels of a granularity in dimension space DrugPrescriptionSpace.

References

  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. 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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Fikes, R., Kehler, T.: The role of frame-based representation in reasoning. Commun. ACM 28(9), 904–920 (1985)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Heer, J., Shneiderman, B.: Interactive dynamics for visual analysis. Commun. ACM 55(4), 45–54 (2012)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. Malinowski, E., Zimányi, E.: Hierarchies in a multidimensional model: from conceptual modeling to logical representation. Data Knowl. Eng. 59(2), 348–377 (2006)

    Article  Google Scholar 

  24. Nebot, V., Llavori, R.B.: Building data warehouses with semantic web data. Decis. Support Syst. 52(4), 853–868 (2012)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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. Niinimäki, M., Niemi, T.: An etl process for olap using rdf/owl ontologies. In: J. Data Semantics [39], pp. 97–119

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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

    Google Scholar 

  39. Spaccapietra, S., Zimányi, E., Song, I.-Y. (eds.): Journal on Data Semantics XIII. LNCS, vol. 5530. Springer, Heidelberg (2009)

    Google Scholar 

  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. Staudt, M., Jarke, M., Jeusfeld, M.A., Nissen, H.W.: Query classes. In: DOOD, pp. 283–295 (1993)

    Google Scholar 

  42. Thalhammer, T., Schrefl, M., Mohania, M.K.: Active data warehouses: complementing OLAP with analysis rules. Data Knowl. Eng. 39(3), 241–269 (2001)

    Article  Google Scholar 

  43. Thollot, R.: Dynamic Situation Monitoring and Context-Aware BI Recommendations. PhD thesis, Ecole Centrale Paris (2012)

    Google Scholar 

  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 

Download references

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

  1. Solvistas GmbH, Graben 18, 4020, Linz, Austria

    Thomas Neuböck

  2. Johannes Kepler University Linz, Altenberger Straße 69, 4040, Linz, Austria

    Bernd Neumayr, Michael Schrefl & Christoph Schütz

Authors
  1. Thomas Neuböck
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernd Neumayr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Schrefl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christoph Schütz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Schrefl .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics

Search

Navigation