Parallel data mining in the HYPERBANK project

S. Fotis, J. A. Keane, R. I. Scott

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    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. © Springer-Verlag Berlin Heidelberg 1999.
    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|Lect. Notes Comput. Sci.
    PublisherSpringer Nature
    Number of pages3
    ISBN (Print)3540664432, 9783540664437
    Publication statusPublished - 1999
    Event5th International Conference on Parallel Processing, Euro-Par 1999 - Toulouse
    Duration: 1 Jul 1999 → …

    Publication series

    NameLecture Notes in Computer Science


    Conference5th International Conference on Parallel Processing, Euro-Par 1999
    Period1/07/99 → …
    Internet address


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