Integrating the development of data mining and data warehouses via model-driven engineering

Data mining is one of the most important analysis techniques to automatically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transformations to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required.

Integrating Evidence-Based Practice in the Training Programme Bachelor’s Degree in Nursing, University Jaume I

The Evidence-Based Practice (EBP) aims to combine a form methodological process of professional experience in health with the most current information on the clinical situation. The professional novice can make better decisions despite lacking sufficient years in clinical practice. We then train the student in correct habits within the methodological process by which you can strengthen both their knowledge and their attitude and ability, allowing secure customs, where all of your work is based on PBE.