Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper 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.

Green education using open educational resources (OER): setting up a green OER repository

A wealth of open educational resources (OER) focused on green topics is currently available through a variety of sources, including learning portals, digital repositories and web sites. However, in most cases these resources are not easily accessible and retrievable, while additional issues further complicate this issue. This paper presents an overview of a number of portals hosting OER, as well as a number of “green” thematic portals that provide access to green OER. It also discusses the case of a new collection that aims to support and populate existing green collections and learning portals respectively, providing information on aspects such as quality assurance/collection and curation policies, workflow and tools for both the content and metadata records that apply to the collection. Two case studies of the integration of this new collection to existing learning portals are also presented.