Automatic Definition of KDD Prototype Processes by Composition
The design of a Knowledge Discovery in Databases (KDD) experiment implies the combined use of several data manipulation tools that are suited for the discovery problem at hand. This implies that users should possess a considerable amount of knowledge and expertise about functionalities and properties of all KDD algorithms implemented in available tools, for choosing the right tools and their proper composition. In order to support users in these demanding activities, we introduce a goal-driven procedure to automatically discover candidate prototype processes by composition of basic algorithms. The core of this procedure is the algorithm matching, which is based on the exploitation of an ontology formalizing the domain of KDD algorithms. The present work focuses on the definition and evaluation of algorithm matching criteria.
KeywordsService Composition Process Composition Approximate Match Prototype Process Compound Data
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