Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
1999 | Euro-Par | hyperbank project,parallel data warehousing tool,data mining,business knowledge modelling,parallel data mining technique,data warehousing,parallel data mining,guiding data mining experiment,parallel computing,high performance banking,generic customer-profiling model,business knowledge,parallel computer,data collection |
Field | DocType | Volume |
Software tool,Data warehouse,Data science,Data mining,Computer science,Profiling (computer programming),Parallel processing,Exploit,Association rule learning,Business knowledge,Financial management | Conference | 1685 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-66443-2 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. Fotis | 1 | 0 | 0.34 |
| J. A. Keane | 2 | 96 | 7.33 |
| R. I. Scott | 3 | 3 | 0.74 |