The University of Alicante (UA), in Spain, is aligned with an Open Government strategy. Within this strategy, UA is carrying out the OpenData4U (Open Data for Universities) project which aims to provided mechanisms to open data from universities, finding out how open data contributes to open government in universities and to encourage reusing open data (not only for the sake of transparency, but also as a basis of novel data-intensive business models related to universities). This paper describes one of the output of the project: an approach for opening data from universities keeping in mind data quality criteria, and tailored to no specific technological scenario. This approach allowed UA to launch its open data portal http://datos.ua.es that is also reviewed in this paper. Also, some research challenges related to university open data are enumerated.

BACKGROUND. The term Rich Internet Applications (RIAs) is generally associated with Web applications that provide the features and functionality of traditional desktop applications. Ten years after the introduction of the term, an ample amount of research has been carried out to study various aspects of RIAs. It has thus become essential to summarize this research and provide an adequate overview. OBJECTIVE. The objective of our study is to assemble, classify, and analyze all RIA research performed in the scientific community, thus providing a consolidated overview thereof, and to identify well-established topics, trends, and open research issues. Additionally, we provide a qualitative discussion of the most interesting findings. This work therefore serves as a reference work for beginning and established RIA researchers alike, as well as for industrial actors that need an introduction in the field, or seek pointers to (a specific subset of) the state-of-the-art. METHOD. A systematic mapping study is performed in order to identify all RIA-related publications, define a classification scheme, and categorize, analyze, and discuss the identified research according to it. RESULTS. Our source identification phase resulted in 133 relevant, peer-reviewed publications, published between 2002 and 2011 in a wide variety of venues. They were subsequently classified according to four facets: development activity, research topic, contribution type, and research type. Pie, stacked bar, and bubble charts were used to depict and analyze the results. A deeper analysis is provided for the most interesting and/or remarkable results. CONCLUSION. Analysis of the results shows that, although the RIA term was coined in 2002, the first RIA-related research appeared in 2004. From 2007 there was a significant increase in research activity, peaking in 2009 and decreasing to pre-2009 levels afterwards. All development phases are covered in the identified research, with emphasis on “design” (33%) and “implementation” (29%). The majority of research proposes a “method” (44%), followed by “model” (22%), “methodology” (18%), and “tools” (16%); no publications in the category “metrics” were found. The preponderant research topic is “models, methods and methodologies” (23%) and, to a lesser extent, “usability and accessibility” and “user interface” (11% each). On the other hand, the topic “localization, internationalization and multilinguality” received no attention at all, and topics such as “deep Web” (under 1%), “business processing”, “usage analysis”, “data management”, “quality and metrics” (all under 2%), “semantics”, and “performance” (slightly above 2%) received very little attention. Finally, there is a large majority of “solution proposals” (66%), few “evaluation research” (14%), and even fewer “validation” (6%), although the latter have been increasing in recent years.

OLAP (On-line Analytical Processing) technologies rely on multidimensional models to provide decision makers with appropriate structures allowing them to intuitively analyze data. However, these multidimensional models may be potentially large, thus becoming too complex to be understood at a glance. Current approaches for OLAP design are focused on providing analysts with a single multidimensional schema derived from their previously stated information requirements, but this is not sufficient to lighten the complexity of the decision making process. To overcome this drawback, the authors propose personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behavior. In this paper, they present a new approach for personalizing OLAP systems at the conceptual level based on the underlying multidimensional model, a user model and a set of personalization rules. Transformations are defined by means of a model-driven strategy to assist in the process of obtaining the corresponding personalized OLAP schemas from these models.