Extending UML for Modeling Data Mining Projects (DM-UML)

Abstract

Existing Data Mining process models propose one way or another of developing projects in a structured manner, trying to reduce their complexity through effective project management. It is well-known in any engineering environment that one of the management tasks that helps to reduce project problems is systematic project documentation, but few of the existing Data Mining processes propose their documentation. Furthermore, these few remark the need of producing documentation at each phase as an input for the next, but they don't show how to do it. On the other hand, in the literature there are examples of UML extensions for data mining projects, but they always focus on the model implementation side and fail to take into account the remainder of the process. In this paper, we present an extension of the UML modeling language for data mining projects (DM-UML) covering all the documentation needs for a project conforming to a standard process, namely CRISP-DM, ranging from business understanding to deployment. We also show an example of a real application of the proposed DM-UML modeling. The result of this approach is that, besides the advantages of having an standardized way of producing the documentation, it clearly constitutes a very useful and transparent tool for modeling and connecting the business understanding or modeling phase with the remainder of the project right through to deployment, as well as a way of facilitating the communication with the nontechnical stakeholders involved in the project, problems which have always been an open question in data mining.

An Empirical Analysis of Productivity Changes in the Ethiopian Commercial Banks: Using DEA- Based Malmquist Productivity Index Approach

“…Consequently, the findings of the study comprehended that the productivity growth of the Egyptian banks was declined over the whole study period at the rate of 2.55%. Baten et al (2015) analyzed the efficiency and total factor productivity changes of the banking sector in Bangladesh based on secondary data using the Malmquist based data envelopment analysis method. The findings revealed that state-owned banks registered the highest cost inefficiency and profit efficiency in comparison to the private banks.…”