O framework de integração do sistema DISCOVER

What we call the beginning is often the end. And to make an end is to make a beginning. The end is where we start from... – T. S. ELIOT, FOUR QUARTETS O ne of human greatest capability is the ability to learn from observed instances of the world and to transmit what have been learnt to others. For thousands of years, we have tried to understand the world, and used the acquired knowledge to improve it. Nowadays, due to the progress in digital data acquisition and storage technology as well as significant progress in the field of Artificial Intelligence — AI, particularly Machine Learning — ML, it is possible to use inductive inference in huge databases in order to find, or discover, new knowledge from these data. The discipline concerned with this task has become known as Knowledge Discovery from Databases — KDD. However, this relatively new research area offers few tools that can efficiently be used to acquire knowledge from data. With these in mind, a group of researchers at the Computational Intelligence Laboratory — LABIC — is working on a system, called DISCOVER, in order to help our research activities in KDD and ML. The aim of the system is to integrate ML algorithms mostly used by the community with the data and knowledge processing tools developed as the results of our work. The system can also be used as a workbench for new tools and ideas. As the main concern of the DISCOVER is related to its use and extension by researches, an important question is related to the flexibility of its architecture. Furthermore, the DISCOVER architecture should allow new tools be easily incorporated. Also, it should impose strong patterns to guarantee efficient component integration. In this work, we propose a component integration framework that aims the development of an integrated computational environment using the tools already implemented in the DISCOVER project. The proposed component integration framework has been developed keeping in mind its future integration with new tools. This framework offers an interface adapter mechanism that creates a layer (horizontal interface) over these tools, a powerful metadata mechanism, which is used to describe both components implementing systems’ functionalities and experiment configurations created by the user, and an environment that enables these experiment execution.