Contributions of KDD to the Knowledge Management Process

Abstract

Knowledge Discovery in Databases (KDD), as any organizational process, is carried out beneath a Knowledge Management (KM) model adopted (even informally) by a corporation. KDD is grossly described in three steps: pre-processing, data mining, and post-processing. The latter is mainly related to the task of transforming in knowledge the patterns issued in the data mining step. On the other hand, KM comprises the following phases, in which knowledge is the subject of the actions: identification of abilities, acquisition, selection and validation, organization and storage, sharing, application, and creation. Although there are many overlaps between KDD and KM, one of them is broadly recognized: the point in which knowledge arises. This paper concerns a study aimed at clarifying relations between the overlapping areas of KDD and knowledge creation, in KM. The work is conducted by means of a case study using the data from the Electoral Court of the Federal District (ECFD), Brazil. The study was developed over a 1.717.000-citizens data set from which data mining models were built by applying algorithms from Weka. It was observed that, although the importance of Information Technology is well recognized in the KM realm, the techniques of KDD deserve a special place in the knowledge creation phase of KM. Moreover, beyond the overlap of post- processing and knowledge creation, other steps of KDD can contribute significantly to KM. An example is the fact that one important decision taken from the ECFD board was taken on the basis of a knowledge acquired from the pre-processing step of KDD.

Keywords

• data mining
• knowledge management
• knowledge discovery in databases