Development of a knowledge base for enabling non-expert users to apply data mining algorithms

Non-expert users find complex to gain richer insights into the increasingly amount of available data. Advanced data analysis techniques, such as data mining, are difficult to apply due to the fact that (i) a great number of data mining algorithms can be applied to solve the same problem, and (ii) correctly applying data mining techniques always requires dealing with the data quality of sources. Therefore, these non-expert users must be informed about what data mining techniques and parameters-setting are appropriate for being applied to their sources according to their data quality. To this aim, we propose the construction of an automatic recommender built using a knowledge base which contains information about previously solved data mining tasks. The construction of the knowledge base is a critical step in the recommender design. We propose a model-driven approach for the development of a knowledge base, which is automatically fed by a Taverna workflow. Experiments are conducted to show the feasibility of our knowledge base as a resource in an online educational platform, in which instructors of e-learning courses are non-expert data miners who need to discover how their courses are used in order to make informed decisions to improve them.