Semantic Enrichment of OLAP Cubes: Multi-dimensional Ontologies and Their Representation in SQL and OWL

  • Bernd Neumayr
  • Christoph Schütz
  • Michael Schrefl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8185)


A multi-dimensional ontology (MDO) enriches an OLAP cube with concepts that represent business terms in the context of data analysis. The formal representation of the meaning of business terms facilitates the unambiguous interpretation of query results as well as the sharing of knowledge among business analysts. In contrast to traditional ontologies, an MDO captures the multi-dimensional, hierarchical world view of business analysts. In this paper, we introduce a translation of MDO concepts to SQL in order to allow for the querying of a closed-world OLAP cube. We introduce a representation in OWL in order to determine subsumption hierarchies of MDO concepts using off-the-shelf reasoners.


Business Intelligence OLAP Data Warehouse Knowledge Representation and Reasoning 


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  1. 1.
    Querying Dimensional Objects, Oracle OLAP User’s Guide, 11g Release 2 (2010),
  2. 2.
    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
  3. 3.
    Berlanga, R., Romero, O., Simitsis, A., Nebot, V., Pedersen, T.B., Abello, A., Aramburu, M.J.: Semantic web technologies for business intelligence (2011)Google Scholar
  4. 4.
    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
  5. 5.
    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. Semantic Web 2(1), 43–53 (2011)Google Scholar
  6. 6.
    Horridge, M., Bechhofer, S.: The OWL API: A Java API for OWL ontologies. Semantic Web 2(1), 11–21 (2011)Google Scholar
  7. 7.
    Khouri, S., Bellatreche, L.: DWOBS: Data warehouse design from ontology-based sources. In: Yu, J.X., Kim, M.H., Unland, R. (eds.) DASFAA 2011, Part II. LNCS, vol. 6588, pp. 438–441. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Lim, L., Wang, H., Wang, M.: Unifying data and domain knowledge using virtual views. In: VLDB, pp. 255–266 (2007)Google Scholar
  9. 9.
    Motik, B., Horrocks, I., Sattler, U.: Bridging the gap between OWL and relational databases. Journal of Web Semantics 7(2), 74–89 (2009)CrossRefGoogle Scholar
  10. 10.
    Nebot, V., Llavori, R.B.: Building data warehouses with semantic web data. Decision Support Systems 52(4), 853–868 (2012)CrossRefGoogle Scholar
  11. 11.
    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., Lee, M.L. (eds.) ER Workshops 2012. LNCS, vol. 7518, pp. 162–171. Springer, Heidelberg (2012)Google Scholar
  12. 12.
    Neumayr, B., Anderlik, S., Schrefl, M.: Towards ontology-based olap: datalog-based reasoning over multidimensional ontologies. In: Song, I.Y., Golfarelli, M. (eds.) DOLAP, pp. 41–48. ACM (2012)Google Scholar
  13. 13.
    Neumayr, B., Schrefl, M., Linner, K.: Semantic Cockpit: An ontology-driven, interactive business intelligence tool for comparative data analysis. In: De Troyer, O., Bauzer Medeiros, C., Billen, R., Hallot, P., Simitsis, A., Van Mingroot, H. (eds.) ER 2011 Workshops. LNCS, vol. 6999, pp. 55–64. Springer, Heidelberg (2011)Google Scholar
  14. 14.
    Pardillo, J., Mazón, J.N.: Using ontologies for the design of data warehouses. International Journal of Database Management Systems 3(2), 73–87 (2011)CrossRefGoogle Scholar
  15. 15.
    Romero, O., Abelló, A.: A framework for multidimensional design of data warehouses from ontologies. Data Knowledge Engineering 69(11), 1138–1157 (2010)CrossRefGoogle Scholar
  16. 16.
    Shearer, R., Motik, B., Horrocks, I.: HermiT: A highly-efficient OWL reasoner. In: Dolbear, C., Ruttenberg, A., Sattler, U. (eds.) OWLED 2008. CEUR Workshop Proceedings, vol. 432. (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bernd Neumayr
    • 1
  • Christoph Schütz
    • 1
  • Michael Schrefl
    • 1
  1. 1.Johannes Kepler University LinzAustria

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