J. Zubcoff

This paper presents a novel approach to integrating data mining models into multidimensional models in order to accomplish the conceptual design of DWs with Association Rules (AR). Expand

We propose a unified modelling language (UML) extension through UML profiles for data-mining that allows us to specify time-series analysis on top of the multidimensional models of data warehouses. Expand

We propose (i) a conceptual model for clustering that helps focusing on the data-mining process at the adequate abstraction level and (ii) an extension of the unified modeling language (UML) by means of the UML profiling mechanism allowing us to design clustering data- mining models on top of the MD model of a DW. Expand

We advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD. Expand

We propose a model-driven approach for the development of a knowledge base, which is automatically fed by a Taverna workflow, which contains information about previously solved data mining tasks. Expand

We present a conceptual model for Classification and a UML profile that allows the design of Classification on MD models. Expand

We propose a new Quality Model for Open Data portals which is exposed from two points of view: qualitative and quantitative. Expand

We present an infrastructure that allows non-expert users to (i) apply user-friendly data mining techniques on big data sources, and (ii) share results as Linked Open Data (LOD) after being semantically annotated as LOD. Expand

We propose an approach to offer non-expert miners a tool that just by uploading their data sets, return them the more accurate mining pattern without dealing with algorithms or settings, thanks to the use of a data mining algorithm recommender. Expand