Unit testing is one of the core practices in the Extreme Programming lightweight software development method, and it is usually carried out with the help of software frameworks that ease the construction of test cases as an integral part of programming tasks. This work describes our first results in studying the integration of automated unit testing practices in conventional 'introduction to programming' laboratories. Since the work used a classical procedural language in the course's assignments, we had to design a specific testing framework called tpUnit. The results of the experiment points out that a straightforward approach for the integration of unit testing in first-semester courses do not result in the expected outcomes in terms of student's engagement in the practice.

Current efforts to standardize e-learning resources are centered on the notion of a learning object as a piece of content that can be reused in diverse educational contexts. Several specifications for the description of learning objects — converging in the LOM standard — have appeared in recent years, providing a common foundation for interoperability and shared semantics. At the same time, the Semantic Web vision has resulted in a number of technologies grounded in the availability of shared, consensual knowledge representations called ontologies. As proposed by several authors, ontologies can be used to provide a richer, logics-based framework for the expression of learning object metadata, resulting in the convergence of both streams of research towards a common objective. In this article, we address the practicalities of the representation of LOM metadata instances into formal ontologies, discussing the main technical and organizational issues that must be addressed for an effective integration of both technologies, and sketching some illustrative examples using modern ontology languages and a large knowledge base.

Current efforts to standardize e-learning resources are centered on the notion of a learning object as a piece of content that can be reused in diverse educational contexts. Several specifications for the description of learning objects — converging in the LOM standard — have appeared in recent years, providing a common foundation for interoperability and shared semantics. At the same time, the Semantic Web vision has resulted in a number of technologies grounded in the availability of shared, consensual knowledge representations called ontologies. As proposed by several authors, ontologies can be used to provide a richer, logics-based framework for the expression of learning object metadata, resulting in the convergence of both streams of research towards a common objective. In this article, we address the practicalities of the representation of LOM metadata instances into formal ontologies, discussing the main technical and organizational issues that must be addressed for an effective integration of both technologies, and sketching some illustrative examples using modern ontology languages and a large knowledge base.

Research Information Systems (RIS) play a critical role in the sharing of scienti¯c information and provide researchers, professionals and decision makers with the required data for their activities. Existing RIS standards have proposed data models to represent the main entities for storage and exchange. These account for the needs of multiple stakeholders through a high°e xibility based on a formal syntax and declared semantics, but for techno-historical reasons they assume the completeness of information within system boundaries. The distributed nature of research information across systems calls for a mechanism to link the local entities from the closed world of concrete RISs with other possibly underspeci¯ed entities exposed through other means, as for example, the Linked Open Data Web. By transformation of a relational model into an open graph model, di®erences between the two system paradigms are revealed. The main principles and techniques for exposing CERIF-driven relational data as linked data will be provided as a¯rst step demonstrating e®ective RISs interconnection through the linked open data (LOD) Web.

Knowledge Management in healthcare covers a number of diverse practice activity areas that range from admission and accounting to preventive health programmes. From among these areas, clinical knowledge management represents a specific category that poses differentiated problems and requires specific management support. Clinical knowledge as practiced today mixes formally assessed scientific knowledge with a person-culture in which the expertise of the clinician is the key element. When considering standard Knowledge Management life cycles, this entails that the required processes of creation, assessment and dissemination of clinical knowledge assets diverge from other kind of activities in how different kinds of knowledge are handled. Further, the Information Technology support required for clinical knowledge assets is complex and multi-perspective, thus requiring schemas that integrate formally gathered evidence and subjective practical knowledge. This paper Sicilia deals with those differences from the viewpoint of formal ontology as a tool to model the specificities of clinical knowledge. An epistemological account of such knowledge is first provided, which serves to delineate how clinical processes and clinical knowledge management could be aligned. The problems of claim evaluation and representation are then approached from that framework, resulting in a realistic integrated set of design guidelines for clinical knowledge management prepared for use in ontology-based Information Systems.

No abstract

Abstract. Fuzzy relational databases have been extensively studied in recent years, resulting in several models and constructs, some of which are implemented as software layers on top of diverse existing database systems. Fuzzy extensions to query languages and end-user query interfaces have also been developed, but the design of programming interfaces has not been properly addressed. In this paper, we describe a software framework called fJdbc that extends the Java Database Connectivity API by allowing fuzzy queries on existing relational databases, using externally-stored metadata. Since the main design objective of this extension is usability for existing database programmers, only a restricted subset of extensions (supported also by an extended object modelling notation) has been included. The overall design of the framework and some experimental data are also described.

Rule-based adaptive hypermedia systems personalize the structure of the hypermedia space using an inference mechanism that operates on a specific knowledge representation about its users. Approximate quantifiers are very frequently used in human language expressions that entail the summarization of a large number of facts. We describe how quantified expressions can be used in adaptation rules to specify common adaptation behaviors, enhancing rule's expressive power for the human expert. Those quantified expressions can be implemented through fuzzy quantification mechanisms operating on fuzzy linguistic labels and relations, and can be integrated as extensions in generalpurpose rule-based adaptive hypermedia systems.