The traditional KDP model has serviced both scientific and business applications, since it was first proposed. However over a period of time, researchers and practitioners have realized the limitations of the model and also felt the need for standardization of the process. We extend the traditional KDP model to enhance it's functionality and propose a novel scheme for Knowledge Discovery in Databases viz. The schema contains the specification of mining requirements and guides the pre-mining operations before actual mining. Data selection, cleaning, transformation and aggregation operations are performed periodically off-line, on the incremental database as it grows.
Aggregation results in extraction of Knowledge Concentrates, which are used for mining.
Actual mining takes place on demand when a mining query is invoked by the user. The scheme is generic, stable and scalable, and inherently takes into account time dimension while mining evolving databases. As an example, we instantiate our model for association rule mining. Complete design and implementation of Knowledge Concentrates, and operators for mining has been done. This results into DBMS like environment for data mining, with schema mapping to conceptual level, Knowledge Concentrate mapping to physical level and development of mining applications at user level.