A Data Warehouse (DW) is a vast collection of historical data built to support multidimensional data analysis applications. In this context, an important problem is that of evolving the implementation (multidimensional, relational) schema of a DW to incorporate new requirements. This paper introduces a conceptual evolution model based on bitemporal versioning of multidimensional schemas, which allows one to modify the DW schema (a) in an implementation-independent manner, and (b) without affecting the operation of existing applications.It also presents a SQL-like language associated to this model, which offers expressions to create and change versions of multidimensional schemas.

A Mobile Collaborative System (MCOS) enable the cooperation of the members of a team to achieve a common goal by using a combination of mobile and fixed technologies. MCOS can be enhanced if the context of the group of users is considered in the execution of activities. This paper proposes a novel model for Context-Aware Mobile COllaborative Systems (CAMCOS) and a functional architecture based on that model. In order to validate both the model and the architecture, a prototype system in the tourism domain was implemented and evaluated.

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Intelligent environments in educational settings are aimed at supporting the learning process with an unobtrusive monitoring of the student while doing his/her activities. A desk is a common object in these settings, so if it is enhanced with sensing capabilities, it would enable gathering information of user-object interaction in a natural and unobtrusive way. An intelligent system is needed to analyze that information to reach conclusions faster than with traditional, manual observational process, and to provide timely valuable information. In this article, a design of a system to semi-automatically identify relationships between behaviors and the task performance of learners from user-object interaction logs is presented. The aim of detecting such relationships is to help teachers and students in the learning process, supporting their activities to identify special needs. Its components are designed to address four main functions: data acquisition, behavior identification, student performance identification, and computing relationships between student task performance and behaviors.