The MiningMart Approach to Knowledge Discovery in Databases

Although preprocessing is one of the key issues in data analysis, it is still common practice to address this task by manually entering SQL statements and using a variety of stand-alone tools. The results are not properly documented and hardly re-usable. The MiningMart system presented in this chapter focusses on setting up and re-using best-practice cases of preprocessing data stored in very large databases. A meta-data model named M4 is used to declaratively define and document both, all steps of such a preprocessing chain and all the data involved. For data and applied operators there is an abstract level, understandable by human users, and an executable level, used by the meta-data compiler to run cases for given data sets. An integrated environment allows for a rapid development of preprocessing chains. Case adaptation to different environments is supported by just specifying all involved database entities in the target DBMS. This allows to re-use best-practice cases published on the Internet. 1 Acquiring Knowledge from Existing Databases The use of very large databases has enhanced in the last years from supporting transactions to additionally reporting business trends. The interest in analyzing the data has increased. One important topic is customer relationship management with the particular tasks of customer segmentation, customer profitability, customer retention, and customer acquisition (e.g. by direct mailing). Other tasks are the prediction of sales in order to minimize stocks, the prediction of electricity consumption or telecommunication services at particular day times in order to minimize the use of external services or optimize network routing, respectively. The health sector demands several analysis tasks for resource management, quality control, ..