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Cluster Computing

, Volume 22, Issue 3, pp 783–803 | Cite as

Designing data cubes in OLAP systems: a decision makers’ requirements-based approach

  • Rahma Djiroun
  • Kamel BoukhalfaEmail author
  • Zaia Alimazighi
Article
  • 154 Downloads

Abstract

Business Intelligence systems rely on an integrated, consistent, and certified information repository called the Data Warehouse (DW) that is periodically fed with operational data. In the decision-making process, the analyzed data are usually stored in the DW in the form of multidimensional cubes. These cubes are queried interactively by the decision makers, according to the online analytical processing paradigm. In larger companies with multiple subsidiaries, the frequent expression of new business needs requires the creation of new data cubes which generate a large number of cubes to be manipulated. The inevitable complexity and heterogeneity of data cubes make it difficult to design data cubes. The decision maker can precisely express his needs through a query in natural language which consists of a set of analysis indicators (measures, dimensions) separated by the AND operator. However, the decision maker’s need may be incomplete. Indeed, he usually has a cube that represents part of his needs and he may want to complete it or enrich it with other cubes that are unknown to him. To deal with these situations, we propose in this paper an approach that addresses the problem of designing and constructing data cubes where the expressed need is scattered over more than one cube. Our goal is to enable decision makers to analyze all of their needs using just one cube. Our approach consists of two variants: a variant that is based on analysis indicators, and another based on the known cube. We present the validation of our approach by means of a tool, called “Design-Cubes-Query” that implements our approach and we show its use through a case study.

Keywords

Business Intelligence Cube design OLAP Multidimensional data cubes Fusion Drill-Across 

References

  1. 1.
    Abelló, A., Samos, J., Saltor, F.: On relationships offering new drill-across possibilities. In: Proceedings of the 5th ACM International Workshop on Data Warehousing and OLAP, pp. 7–13. ACM (2002)Google Scholar
  2. 2.
    Alberto, D.: Fusion cubes: towards self-service business intelligence. Int. J. Data Warehous. Min. 9(2), 66–88 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bimonte, S., Sautot, L., Journaux, L., Faivre, B.: Multidimensional model design using data mining: a rapid prototyping methodology. Int. J. Data Warehous. Min. 13(1), 1–35 (2017)CrossRefGoogle Scholar
  4. 4.
    Boukraâ, D., Boussaïd, O., Bentayeb, F.: OLAP operators for complex object data cubes. In: ADBIS, pp. 103–116. Springer (2010)Google Scholar
  5. 5.
    Cheung, D.W., Zhou, B., Kao, B., Lu, H., Lam, T.W., Ting, H.F.: Requirement-based data cube schema design. In: Proceedings of the Eighth International Conference on Information and Knowledge Management, pp. 162–169. ACM (1999)Google Scholar
  6. 6.
    Chhabra, R., Pahwa, P.: Data mart designing and integration approaches. Int. J. Comput. Sci. Mob. Comput. 3(4), 74–79 (2014)Google Scholar
  7. 7.
    Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. In: KDD Workshop on Data Cleaning and Object Consolidation, vol. 3, pp. 73–78 (2003)Google Scholar
  8. 8.
    Djiroun, R., Bimonte, S., Boukhalfa, K.: A first framework for top-k cubes queries. In: International Conference on Conceptual Modeling, pp. 187–197. Springer (2015)Google Scholar
  9. 9.
    Etcheverry, L., Vaisman, A., Zimányi, E.: Modeling and querying data warehouses on the semantic web using QB4OLAP. In: International Conference on Data Warehousing and Knowledge Discovery, pp. 45–56. Springer (2014)Google Scholar
  10. 10.
    Gardner, S.R.: Building the data warehouse: the tough questions project managers have to ask their companies’ executives–and themselves–and the guidelines needed to sort out the answers. Commun. ACM 41(9), 52–61 (1998)CrossRefGoogle Scholar
  11. 11.
    Ghrab, A., Romero, O., Skhiri, S., Vaisman, A., Zimányi, E.: A framework for building OLAP cubes on graphs. In: East European Conference on Advances in Databases and Information Systems, pp. 92–105. Springer (2015)Google Scholar
  12. 12.
    Golfarelli, M., Rizzi, S.: A methodological framework for data warehouse design. In: Proceedings of the 1st ACM International Workshop on Data Warehousing and OLAP, pp. 3–9. ACM (1998)Google 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.
    Gomaa, W.H., Fahmy, A.A.: A survey of text similarity approaches. Int. J. Comput. Appl. (2013).  https://doi.org/10.5120/11638-7118
  15. 15.
    Hung, E., Cheung, D.W., Kao, B.: Optimization in data cube system design. J. Intell. Inf. Syst. 23(1), 17–45 (2004)CrossRefzbMATHGoogle Scholar
  16. 16.
    Hüsemann, B., Lechtenbörger, J., Vossen, G.: Conceptual Data Warehouse Design. Universität Münster, Angewandte Mathematik und Informatik (2000)Google Scholar
  17. 17.
    Islam, A., Inkpen, D.: Semantic text similarity using corpus-based word similarity and string similarity. ACM Trans. Knowl. Discov. Data 2(2), 10 (2008)CrossRefGoogle Scholar
  18. 18.
    Jindal, R., Taneja, S.: Comparative study of data warehouse design approaches: a survey. Int. J. Database Manag. Syst. 4(1), 33 (2012)CrossRefGoogle Scholar
  19. 19.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. Wiley, New York (2011)Google Scholar
  20. 20.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley, New York (2013)Google Scholar
  21. 21.
    Masuma, M.R., Losarwar, V.: Text classification and clustering through similarity measures. IJLTEMAS 5(3), 91–94 (2016)Google Scholar
  22. 22.
    Nedelcu, B.: Business intelligence systems. Database Syst. J. 4(4), 12–20 (2013)Google Scholar
  23. 23.
    Niemi, T., Nummenmaa, J., Thanisch, P.: Constructing OLAP cubes based on queries. In: Proceedings of the 4th ACM International Workshop on Data Warehousing and OLAP, pp. 9–15. ACM (2001)Google Scholar
  24. 24.
    Parimala, N., Pahwa, P.: Coalescing data marts. In: Proceedings of XVI International Conference on Computer and Information Science and Engineering, pp. 280–285 (2006)Google Scholar
  25. 25.
    Djiroun, R., Boukhalfa, K., Alimazighi, Z., et al.: A data cube design and construction methodology based on OLAP queries. In: 13th IEEE/ACS International Conference of Computer Systems and Applications, AICCSA 2016, Agadir, Morocco, pp. 1–8 (2016)Google Scholar
  26. 26.
    Riazati, D., Thom, J.A., Zhang, X.: Drill across & visualization of cubes with non-conformed dimensions. In: Proceedings of the Nineteenth Conference on Australasian Database, vol. 75, pp. 97–105. Australian Computer Society, Inc. (2008)Google Scholar
  27. 27.
    Rizzi, S., Abelló, A., Lechtenbörger, J., Trujillo, J.: Research in data warehouse modeling and design: dead or alive? In: Proceedings of the 9th ACM International Workshop on Data Warehousing and OLAP, pp. 3–10. ACM (2006)Google Scholar
  28. 28.
    Sabaini, A., Zimányi, E., Combi, C.: Extending the multidimensional model for linking cubes. In: EDA, pp. 17–32 (2015)Google Scholar
  29. 29.
    Bimonte, S., Schneider, M.: Merging spatial data cubes using the GIS overlay operator. J. Decis. Syst. 19(3), 261–290 (2010)CrossRefGoogle Scholar
  30. 30.
    Torlone, R.: Two approaches to the integration of heterogeneous data warehouses. Distrib. Parallel Databases 23(1), 69–97 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratory LSIUSTHBAlgiersAlgeria

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