Towards a model-driven engineering approach of data mining

Nowadays, data mining is based on low-level specications of the employed techniques typically bounded to a specic analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Here, we propose a model-driven approach 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 (via data-warehousing technology) and the analysis models for data mining (tailored to a specic platform). Thus, analysts can concentrate on the analysis problem via conceptual data-mining models instead of low-level programming tasks related to the underlying-platform technical details. These tasks are now entrusted to the model-transformations scaffolding.

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.

Size-Dependent and Step-Modulated Supramolecular Electrochemical Properties of Catechol-Derived Adlayers at Pt(hkl) Surfaces

The electrochemical reactivity of catechol-derived adlayers is reported at platinum (Pt) single-crystal electrodes. Pt(111) and stepped vicinal surfaces are used as model surfaces possessing well-ordered nanometer-sized Pt(111) terraces ranging from 0.4 to 12 nm. The electrochemical experiments were designed to probe how the control of monatomic step-density and of atomic-level step structure can be used to modulate molecule–molecule interactions during self-assembly of aromatic-derived organic monolayers at metallic single-crystal electrode surfaces. A hard sphere model of surfaces and a simplified band formation model are used as a theoretical framework for interpretation of experimental results. The experimental results reveal (i) that supramolecular electrochemical effects may be confined, propagated, or modulated by the choice of atomic level crystallographic features (i.e.monatomic steps), deliberately introduced at metallic substrate surfaces, suggesting (ii) that substrate-defect engineering may be used to tune the macroscopic electronic properties of aromatic molecular adlayers and of smaller molecular aggregates.