Advertisement

Modelling Multidimensional Data in a Dataflow-Based Visual Data Analysis Environment

  • Frank Wietek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1626)

Abstract

Multidimensional data analysis is currently being discussed in terms like OLAP, data warehousing, or decision support, mainly concentrating on business applications. Numerous OLAP-tools providing flexible query facilities for datacubes are being designed and distributed. Typical analysis sessions with these kind of systems comprise long and branching sequences of exploratory analysis steps which base upon each other. While concentrating on single functions and processing steps, management of this analysis process as a whole is scarcely supported.

This paper proposes a dataflow-based visual programming environment for multidimensional data analysis (VIOLA) as an approach to deal with this problem. Providing a foundation of basic operations, data processing, navigation, and user interaction, an appropriate data model (MADEIRA) is developed. Epidemiological studies, i. e. investigations of aggregate data on populations, their state of health, and potential risk factors, will serve as a leading example of a typical application area.

Keywords

Aggregation Function Category Attribute Multidimensional Data Graph Node Analysis Session 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. [1]
    S. Chaudhuri and U. Dayal. An overview of data warehousing and OLAP technology. SIGMOD Record, 26(1):65–74, 1997.CrossRefGoogle Scholar
  2. [2]
    E. Woods, E. Kyral, et al. OVUM evaluates OLAP. OVUM Ltd, 1996.Google Scholar
  3. [3]
    M. Blaschka, C. Sapia, G. Höfling, and B. Dinter. Finding your way through multidimensional data models. In Proc. Int. Workshop on Data Warehouse Design and OLAP Technology (DWDOT), Wien, 1998.Google Scholar
  4. [4]
    D. J. Hand. Intelligent data analysis: Issues and opportunities. In Advances in Intelligent Data Analysis (Proc. IDA’97), pages 1–14. Springer Verlag, 1997.Google Scholar
  5. [5]
    D. D. Hils. Visual languages and computing survey: Data flow visual programming languages. Journal on Visual Languages and Computing, 3(1):69–101, 1993.CrossRefGoogle Scholar
  6. [6]
    F. Wietek. Die Epi-Workbench–ein graphischer Editor zur Modellierung deskriptiver epidemiologischer Studien. KI-Journal. Schwerpunkt KI und Medizin, 3:27–31, 1997.Google Scholar
  7. [7]
    L. Cabibbo and R. Torlone. A logical approach to multidimensional databases. In 6th Int. Conf. on Extending Database Technology (EDBT), pages 183–197. Springer Verlag, 1998.Google Scholar
  8. [8]
    W. Lehner, J. Albrecht, and H. Wedekind. Normal forms for multidimensional databases. In 10th Int. Conf. on Scientific and Statistical Database Management (SSDBM), pages 63–72. IEEE Press, 1998.Google Scholar
  9. [9]
    H.-J. Lenz and A. Shoshani. Summarizability in OLAP and statistical data bases. In 9th Int. Conf. on Scientific and Statistical Database Management (SSDBM), pages 132–143. IEEE Press, 1997.Google Scholar
  10. [10]
    R. R. Springmeyer, M. M. Blattner, and N. L. Max. A characterization of the scientific data analysis process. In Proc. IEEE Visualization 1992, pages 235–242.Google Scholar
  11. [11]
    B. Shneiderman. Dynamic queries for visual information seeking. IEEE Software, 11(6):70–77, 1994.CrossRefGoogle Scholar
  12. [12]
    W. S. Cleveland and M. E. McGill, editors. Dynamic Graphics for Statistics. Wadsworth & Brooks / Cole Advanced Books and Software, Belmont, CA, 1988.Google Scholar
  13. [13]
    B. Lucas, G. D. Abram, et al. An architecture for a scientific visualization system. In Proc. IEEE Visualization 1992, pages 107–114.Google Scholar
  14. [14]
    C. Upson, J. Faulhaber, et al. The application visualization system: A computational environment for scientific visualization. IEEE Computer Graphics and Applications, 9(4):30–42, 1989.CrossRefGoogle Scholar
  15. [15]
    P. G. Selfridge, D. Srivastava, and L. O. Wilson. IDEA: Interactive data exploration and analysis. In Proc. SIGMOD’96, pages 24–35, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Frank Wietek
    • 1
  1. 1.Department of Computer ScienceUniversity of OldenburgOldenburgGermany

Personalised recommendations