Parallel Data Mining in the HYPERBANK Project⋆

  • S. Fotis
  • J. A. Keane
  • R. I. Scott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1685)


The aim of the High Performance Banking (HYPERBANK) project is to provide the banking sector with the requisite toolset for the increased understanding of existing and prospective customers. The approach exploits and integrates three areas: business knowledge modelling, data warehousing and data mining, together with parallel computing. Business knowledge modelling formally describes the enterprise in terms of roles, goals and rules. A generic customer-profiling model has been produced and has been instrumental in informing and guiding data mining experiments performed on the banks’ data. Parallel computing is required to manipulate and analyse to maximum effect the vast amounts of data collected by banks. A parallel data warehousing tool has been produced and work is ongoing to integrate the customer profiling model with this tool. In this paper, we present work done in the development and implementation of a variety of parallel data mining techniques.


Data Mining Association Rule Parallel Machine Banking Sector Data Warehousing 
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.


  1. [1]
    J. Shafer, R. Agrawal and M. Mehta. SPRINT: A scalable Parallel Classiffier for Data Mining. In Proceedings of the 22nd VLDB Conference, Mumbai (Bombay),India, 1996.Google Scholar
  2. [2]
    R. Agrawal and J. Shafer. Parallel Mining of Association Rules: Design, Implementation and Experience. IBM Research Report RJ 10004, 1996.Google Scholar
  3. [3]
    R. Clarke, D. Filippidou, P. Kardasis, P. Loucopoulos and R. Scott. “Integrating the Management of Business Domain and Discovered Knowledge”. In Proc. of Panhellenic Conference on New Information Technology, NIT’98, Athens, October 1998.Google Scholar
  4. [4]
    D. Filippidou, J.A. Keane, S. Svinterikou and J. Murray. DataMining for Business Process Management: Applying the HyperBank Approach. In Proceedings of the 2nd International Conference on The Practical Application of Data Discovery and Data Mining, The Practical Application Company Ltd., 1998.Google Scholar
  5. [5]
  6. [6]
    J.A. Keane and R. Scott. Parallel Link Analysis. Department of Computation, UMIST, Manchester, UK, 1999. in preparation.Google Scholar
  7. [7]
    A.J. Ferrari. JPVM: Network Parallel Computing in Java. Technical Report CS-97-29, Department of Computer Science, University of Virginia, USA. 1997.Google Scholar
  8. [8]
    S. Anahory and D. Murray. Data Warehousing in the Real World: A Practical Guide for Building Decision Support Systems. Addison-Wesley, 1997.Google Scholar
  9. [9]
    U.S. Congress, Office of Technology Assessment. Information Technologies for the Control of Money Laundering, OTA-ITC-630, Washington DC, US Government Printing Office, September, 1995.Google Scholar
  10. [10]
    U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth. From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996.Google Scholar
  11. [11]
    J.A. Keane. High Performance Banking. In Proc. of RIDE’ 97, IEEE Press, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • S. Fotis
    • 1
  • J. A. Keane
    • 1
  • R. I. Scott
    • 1
  1. 1.Department of Computation UMISTUK

Personalised recommendations