Organizations are beginning to apply data mining and knowledge discovery techniques to their corporate data sets, thereby enabling the identification of trends and the discovery of inductive knowledge. Since traditional transaction databases are not optimized for analytical processing, they must be transformed. This article proposes the use of modular components to decrease the overall amount of human processing and intervention necessary for the transformation process. Our approach configures components to extract data-sets using a set of "extraction hints." Our framework incorporates decentralized, generic components that are reusable across domains and databases. Finally, we detail an implementation of our component-based framework for an aviation data set.
ASJC Scopus subject areas
- Computer Science(all)