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.

Towards a reverse engineering approach for guiding user in applying data mining

Comunicación presentada en las XVI Jornadas de Ingeniería del Software y Bases de Datos, JISBD 2011, A Coruña, 5-7 septiembre 2011.

Forensic analysis of buildings affected by mining subsidence based on Differential Interferometry (Part III)

This work presents a forensic analysis of buildings affected by mining subsidence, which is based on deformation data obtained by Differential Interferometry (DInSAR). The proposed test site is La Union village (Murcia, SE Spain) where subsidence was triggered in an industrial area due to the collapse of abandoned underground mining labours occurred in 1998. In the first part of this work the study area was introduced, describing the spatial and temporal evolution of ground subsidence, through the elaboration of a cracks map on the buildings located within the affected area. In the second part, the evolution of the most significant cracks found in the most damaged buildings was monitored using biaxial extensometric units and inclinometers. This article describes the work performed in the third part, where DInSAR processing of satellite radar data, available between 1998 and 2008, has permitted to determine the spatial and temporal evolution of the deformation of all the buildings of the study area in a period when no continuous in situ instrumental data is available. Additionally, the comparison of these results with the forensic data gathered in the 2005–2008 period, reveal that there is a coincidence between damaged buildings, buildings where extensometers register significant movements of cracks, and buildings deformation estimated from radar data. As a result, it has been demonstrated that the integration of DInSAR data into forensic analysis methodologies contributes to improve significantly the assessment of the damages of buildings affected by mining subsidence.