Author(s)

B Tramontana

Abstract

The importance of a \“Data Mining oriented analysis phase” in a data warehousing project methodology B. Tramontana AIA Data Processing Center, Rome, Italy. Abstract Classical two or three level architectures proposed so far for data warehouses show some drawbacks when adopted to work over large numbers of heterogeneous operational sources. In the application context under consideration, having a suitable architecture may not be enough for design purposes. Indeed, data warehouse and data mining architectural substrate design in very large operational environments can be a quite hard problem to be attacked with traditional manual methodologies. Therefore, the purpose of this paper is to provide an alternative four-level data warehouse architecture (DMOIS) and a methodology phase, partially data mining based and oriented. 1 Introduction Many industrial, commercial and scientific organizations accumulate a large quantity of data in their own activities, but a great part of these cannot be used easily; therefore they require a basic skill in the data extraction and in the subsequent information delivery (KDD process). 2 Knowledge discovery and data mining The term \“Knowledge Discovery in Databases” (KDD) puts in evidence the process of knowledge acquisition which, in the knowledge management field, allows the business manager to find out the information, useful to reach the business targets, in a short time.

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