Skip to main content

A Flexible Report Architecture Based on Association Rules Mining

  • Conference paper
Advanced Data Mining and Applications (ADMA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3584))

Included in the following conference series:

Abstract

This paper proposes flexible report architecture based on association rules data mining. A three-layer architecture is proposed namely, origin-data layer, data-processing layer, and format layer. These three layers are linked by a data variant tree in a power information management system. Users can modify report format as well as data whenever needed. In the origin-data layer data warehouse is used to provide data from multiple databases. In the data-processing layer, on-line analytical processing (OLAP) and association rules are used to enhance the template-making for reports. A smart solution to the problem of fixed report templates is provided and information in a power information management system can be shared. In some sense it can be an all-purpose tool to generate reports with great flexibility.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Zhou, Y., Deng, Y.: Design and application of a platform independent spreadsheet tool for power system. Power system technology 26(5), 57–61 (2002)

    MathSciNet  Google Scholar 

  2. Bertoli, M., Stranieri, A.: Forecasting on complex datasets with association rules. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3213, pp. 1171–1180. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Ammoura, A., Zaïane, O., Goebe, R.: Towards a novel OLAP interface for distributed data warehouses. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, p. 174. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  4. Wang, S.L., et al.: A try for handling uncertainties in spatial data mining. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3215, pp. 513–520. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Ceglar, A., Roddick, J., Calder, P., Rainsford, C.: Visualizing hierarchical associations. Knowledge and Information Systems. Springer, Berlin (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hu, Q. (2005). A Flexible Report Architecture Based on Association Rules Mining. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_87

Download citation

  • DOI: https://doi.org/10.1007/11527503_87

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27894-8

  • Online ISBN: 978-3-540-31877-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics