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In the last decade, there lias been a large growth in the capacity to generate and collect data, mainly due to three factors: first, the constant increase in the processing power of computers; second, the continuing storage of a large quantities of data at low costs; and last, the introduction of new and better technologies related to processing and transmitting data. Although database management systems offer basic tools for optimizing the storing and searching in large quantities of data, it is still difficult to help humans understand and analyze tese large data structures. In this context, the process of Knowledge Discovery in Databases — KDD — lias emerged as a new technology oriented towards comprehending and searching for lcnowledge intrinsic to these large masses of data, mostly by using various techniques based on statistics, databases, visualization tools and Machine Learning. Generally spealcing, the KDD process is composed of various phases, starting with the defmition of the domain, pre-processing the data, a phase of Data Miráng and, fmally, analyzing and interpreting the Imowledge that was obtained. This work tries to comprehend and separate the different phases in the KDD process, analyzing the role of the Data Mining phase in this process. Two case studies (one of a petroleuirt database and another of a database for the Program of Genetic Improving for the Nelore Breed) were made to emphasize this point. This work also loolcs at some important aspects, such as the relevance of discretizing continuous data so as to obtain better classification mies, as well as showing the KDD process applied to a real database, indicating the problems that were found and the importance of the presence of a domain expert for this process to be successfuL

Data Mining no Processo de Extração de Conhecimento de Bases de Dados