Quantitative Redundancy in Partial Implications

We survey the different properties of an intuitive notion of redundancy, as a function of the precise semantics given to the notion of partial implication. The final version of this survey will appear in the Proceedings of the Int. Conf. Formal Concept Analysis, 2015.Comment: Int. Conf. Formal Concept Analysis, 201

Iterator-based algorithms in self-tuning discovery of partial implications

We describe the internal algorithmics of our recent implementation of a closure-based self-tuning associator: yacaree. This system is designed so as not to request the user to specify any threshold. In order to avoid the need of a support threshold, we introduce an algorithm that constructs closed sets in order of decreasing support; we are not aware of any similar previous algorithm. In order not to overwhelm the user with large quantities of partial implications, our system filters the output according to a recently studied lattice-closure-based notion of con fidence boost, and self-adjusts the threshold for that rule quality measure as well. As a consequence, the necessary algorithmics interact in complicated ways. In order to control this interaction, we have resorted to a well-known, powerful conceptual tool, called Iterators: this notion allows us to distribute control among the various algorithms at play in a relatively simple manner, leading to a fully operative, open-source, effi cient system for discovery of partial implications in relational data.Postprint (published version

Elearning, Communication and Open-data: Massive Mobile, Ubiquitous and Open Learning

ABSTRACT: In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided

ECO D2.5 Learning analytics requirements and metrics report

In MOOCs, learning analytics have to be addressed to the various types of learners that participate. This deliverable describes indicators that enable both teachers and learner to monitor the progress and performance as well as identify whether there are learners at risk of dropping out. How these indicators should be computed and displayed to end users by means of dashboards is also explained. Furthermore a proposal based on xAPI statements for storing relevant data and events is provided.Part of the work carried out has been funded with support from the European Commission, under the ICT Policy Support Programme, as part of the Competitiveness and Innovation Framework Programme (CIP) in the ECO project under grant agreement n° 21127

Desarrollo Eficiente de Lenguajes Específicos de Dominio para la Ejecución de Procesos de Minería de Datos

Aunque las técnicas de minería de datos están consiguiendo cada día una mayor popularidad, su complejidad impide que sean aún utilizables por personas sin un sólido conocimiento en las mismas. Una posible solución, ya explorada por los autores de este artículo, es la construcción de Lenguajes Específicos de Dominio que proporcionen una serie de primitivas de alto nivel para la ejecución de procesos de minería de datos. Dichas primitivas sólo hacen referencia a terminología propia del dominio analizado, enmascarando detalles técnicos de bajo nivel. No obstante, la construcción de un lenguaje específico de dominio puede ser un proceso costoso. Este artículo muestra cómo reducir los tiempos de desarrollo de estos lenguajes de análisis mediante la reutilización de partes comunes de estos DSLs.Este trabajo ha sido parcialmente financiado por el Gobierno de Cantabria (España) mediante el Programa de Personal Investigador en Formación Predoctoral de la Universidad de Cantabria y por el Gobierno de España en el proyecto TIN2014-56158-C4-2-P(M2C2)