Business intelligence to support NOVA IMS academic services BI system

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceKimball argues that Business Intelligence is one of the most important assets of any organization, allowing it to store, explore and add value to the organization’s data which will ultimately help in the decision making process. Nowadays, some organizations and, in this specific case, some schools are not yet transforming data into their full potential and business intelligence is one of the most known tools to help schools in this issue, seen as some of them are still using out-dated information systems, and do not yet apply business intelligence techniques to their increasing amounts of data so as to turn it into useful information and knowledge. In the present report, I intend to analyse the current NOVA IMS academic services data and the rationales behind the need to work with this data, so as to propose a solution that will ultimately help the school board or the academic services to make better-supported decisions. In order to do so, it was developed a Data Warehouse that will clean and transform the source database. Another important step to help the academic services is to present a series of reports to discover information in the decision making process

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Extending the learning object definition to represent programming problems

The present generation of eLearning platforms values the interchange of learning objects standards. Nevertheless, for specialized domains these standards are insufficient to fully describe all the assets, especially when they are used as input for other eLearning services. To address this issue we extended an existing learning objects standard to the particular requirements of a specialized domain, namely the automatic evaluation of programming problems. The focus of this paper is the definition of programming problems as learning objects. We introduce a new schema to represent metadata related to automatic evaluation that cannot be conveniently represented using existing standards, such as: the type of automatic evaluation; the requirements of the evaluation engine; or the roles of different assets - tests cases, program solutions, etc. This new schema is being used in an interoperable repository of learning objects, called crimsonHex.European Comissio

The Athabasca University edusource Project: Building An Accessible Learning Object Repository

Athabasca University - Canada's Open University (AU) made the commitment to put all of its courses online as part of its Strategic University Plan. In pursuit of this goal, AU participated in the eduSource project, a pan-Canadian effort to build the infrastructure for an interoperable network of learning object repositories. AU acted as a leader in the eduSource work package, responsible for the metadata and standards for learning objects. In addition, the team of professionals, academics, librarians and other researchers worked to create an accessible repository of learning objects across university departments and subjects. Most critically, the team worked beyond the development of a learning object repository and considered the adaptation of content and related applications, pedagogical approaches and the use of learning objects by instructional designers, faculty and the learners themselves. This paper describes one institution's approach to learning object repository development, from a technical and pedagogical perspective, along with some of the lessons learned during the process

Ontology-based knowledge representation of experiment metadata in biological data mining

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Three Denerations of DBMS

This paper describes the evolution of data base technology from early computing to the sophisticated systems of today. It presents an overview of the most popular data base management systems architectures such as hierarchical, network, relational and object-oriented. The last section of this paper presents a view of the factors that will influence the future of data base technology