Towards Semantic Assessment of Summarizability in Self-service Business Intelligence

  • Luis-Daniel Ibáñez
  • Jose-Norberto MazónEmail author
  • Elena Simperl
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 767)


Traditional Business Intelligence solutions allow decision makers to query multidimensional data cubes by using OLAP tools, thus ensuring summarizability, which refers to the possibility of accurately computing aggregation of measures along dimensions. With the advent of the Web of Open Data, new external sources have been used in Self-service Business Intelligence for acquiring more insights through ad-hoc multidimensional open data cubes. However, as these data cubes rely upon unknown external data, decision makers are likely to make meaningless queries that lead to summarizability problems. To overcome this problem, in this paper, we propose a framework that automatically extracts multidimensional elements from SPARQL query logs and creates a knowledge base to detect semantic correctness of summarizability.


OLAP Data cube Summarizability Open data 



Jose-Norberto Mazón is funded by grant number JC2015-00284 under “José Castillejo” research program from Spanish Government (Ministerio de Educación, Cultura y Deporte en el marco del Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+i, Subprograma Estatal de Movilidad, del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016). This work is also funded by research project TIN2016-78103-C2-2-R from Spanish Government (Ministerio de Economía, Industria y Competitividad).


  1. 1.
    Abelló, A., Darmont, J., Etcheverry, L., Golfarelli, M., Mazón, J.N., Naumann, F., Pedersen, T., Rizzi, S.B., Trujillo, J., Vassiliadis, P., Vossen, G.: Fusion cubes: towards self-service Business Intelligence. Int. J. Data Warehous. Min. 9(2), 66–88 (2013)CrossRefGoogle Scholar
  2. 2.
    Abello, A., Romero, O., Pedersen, T.B., Berlanga, R., Nebot, V., Aramburu, M.J., Simitsis, A.: Using semantic web technologies for exploratory OLAP: a survey. IEEE Trans. Knowl. Data Eng. 27(2), 571–588 (2015)CrossRefGoogle Scholar
  3. 3.
    Cyganiak, R., Reynolds, D., Tennison, J.: The RDF data cube vocabulary. Technical report, W3C (2014).
  4. 4.
    Etcheverry, L., Vaisman, A., Zimányi, E.: Modeling and Querying Data Warehouses on the Semantic Web Using QB4OLAP. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 45–56. Springer, Cham (2014). doi: 10.1007/978-3-319-10160-6_5 Google Scholar
  5. 5.
    Golfarelli, M., Rizzi, S.: Data Warehouse Design: Modern Principles and Methodologies. McGraw-Hill Inc., New York (2009)Google Scholar
  6. 6.
    Höffner, K., Lehmann, J., Usbeck, R.: CubeQA - question answering on RDF data cubes. In: International Semantic Web Conference (ISWC) (2016)Google Scholar
  7. 7.
    Horner, J., Song, I.-Y.: A Taxonomy of Inaccurate Summaries and Their Management in OLAP Systems. In: Delcambre, L., Kop, C., Mayr, H.C., Mylopoulos, J., Pastor, O. (eds.) ER 2005. LNCS, vol. 3716, pp. 433–448. Springer, Heidelberg (2005). doi: 10.1007/11568322_28 CrossRefGoogle Scholar
  8. 8.
    Kämpgen, B., O’Riain, S., Harth, A.: Interacting with statistical linked data via OLAP operations. In: Simperl, E., Norton, B., Mladenic, D., Della Valle, E., Fundulaki, I., Passant, A., Troncy, R. (eds.) ESWC 2012. LNCS, vol. 7540, pp. 87–101. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-46641-4_7
  9. 9.
    Kimball, R., Ross, M.: The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence. Wiley, Indianapolis (2010)Google Scholar
  10. 10.
    Lebo, T., Sahoo, S., McGuinness, D.: PROV-O: the PROV ontology. Technical report, W3C (2013).
  11. 11.
    Lenz, H.J., Shoshani, A.: Summarizability in OLAP and statistical data bases. In: Proceedings of the Ninth International Conference on Scientific and Statistical Database Management, pp. 132–143. IEEE Computer Society,., January 1997Google Scholar
  12. 12.
    Luczak-Roesch, M., Aljaloud, S., Berendt, B., Hollink, L.: Usewod 2016 research dataset. doi: 10.5258/SOTON/385344
  13. 13.
    Mazón, J.N., Lechtenbörger, J., Trujillo, J.: A survey on summarizability issues in multidimensional modeling. Data Knowl. Eng. 68(12), 1452–1469 (2009)CrossRefGoogle Scholar
  14. 14.
    Nebot, V., Berlanga, R.: Statistically-driven generation of multidimensional analytical schemas from linked data. Knowl. Based Syst. 110, 15–29 (2016)CrossRefGoogle Scholar
  15. 15.
    Niemi, T., Niinimäki, M., Thanisch, P., Nummenmaa, J.: Detecting summarizability in OLAP. Data Knowl. Eng. 89, 1–20 (2014)CrossRefGoogle Scholar
  16. 16.
    Pedersen, T.B.: Managing Complex Multidimensional Data. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 1–28. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36318-4_1 CrossRefGoogle Scholar
  17. 17.
    Rafanelli, M., Bezenchek, A., Tininini, L.: The aggregate data problem: a system for their definition and management. ACM Sigmod Rec. 25(4), 8–13 (1996)CrossRefGoogle Scholar
  18. 18.
    Varga, J., Etcheverry, L., Vaisman, A.A., Romero, O., Pedersen, T.B., Thomsen, C.: Qb2olap: Enabling olap on statistical linked open data. In: 32nd International Conference on Data Engineering (ICDE), pp. 1346–1349. IEEE (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Luis-Daniel Ibáñez
    • 1
  • Jose-Norberto Mazón
    • 2
    Email author
  • Elena Simperl
    • 1
  1. 1.University of SouthamptonSouthamptonUK
  2. 2.DLSIUniversidad de AlicanteAlicanteSpain

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