S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

Data mining has proven to be very useful in order to extract information from data in many different contexts. However, due to the complexity of data mining techniques, it is required the know-how of an expert in this field to select and use them. Actually, adequately applying data mining is out of the reach of novice users which have expertise in their area of work, but lack skills to employ these techniques. In this paper, we use both model-driven engineering and scientific workflow standards and tools in order to develop named S3Mining framework, which supports novice users in the process of selecting the data mining classification algorithm that better fits with their data and goal. To this aim, this selection process uses the past experiences of expert data miners with the application of classification techniques over their own datasets. The contributions of our S3Mining framework are as follows: (i) an approach to create a knowledge base which stores the past experiences of experts users, (ii) a process that provides the expert users with utilities for the construction of classifiers? recommenders based on the existing knowledge base, (iii) a system that allows novice data miners to use these recommenders for discovering the classifiers that better fit for solving their problem at hand, and (iv) a public implementation of the framework?s workflows. Finally, an experimental evaluation has been conducted to shown the feasibility of our framework

Un marco para democratizar la minería de datos: propuesta inicial y retos

Movimientos como el de datos abiertos posibilitan que cada vez haya una mayor disponibilidad de datos accesibles para su reutilización. A pesar de que el número de herramientas analíticas que están a nuestra disposición crece cada día, lamentablemente ninguna permite realizar un proceso de extracción de conocimiento directo a usuarios con poca o nula experiencia en el uso de la estadística y de algoritmos de minería de datos. En este artículo se presenta una aproximación a un marco KaaS (Knowledge as a Service) que posibilite a usuarios no expertos la extracción de conocimiento a partir de un conjunto de datos. Se muestra que la propuesta es viable y se plantean los retos aún abiertos

Evolution of Posidonia oceanica seagrass meadows and its implications for management

Results of the monitoring network of the Posidonia oceanica meadows in the Valencia region in Spain are analysed. For spatial comparison the whole data set has been analysed, however, for temporal trends we only selected stations that have been monitored at least 6 years in the period of 2002–2011 (26 stations in 13 localities). At the south of the studied area, meadows are larger, and they have higher density and covering than that in the Valencia Gulf, excluding Oropesa meadow. Monitoring of P. oceanica meadows in the Valencia region in Spain indicates that most of them are stationary or they are increasing their density and covering while no decline was observed in the studied meadows. These results indicate that there is not a general decline of P. oceanica meadows and that the decline of P. oceanica, when it has been observed in other studies, is produced by local causes that may be managed at the local level. This study also reflects the importance of long series of direct data to analyse trends in the population dynamics for slow-growing species.Diputación de Alicante, Municipalities of El Campello Alicante and Calpe and the Spanish Ministry of Agriculture Food and Environment