Ref HAL: hal-00712590_v1
DOI: 10.1016/j.eswa.2012.04.040
Résumé: In this paper, we present CatViz—Temporally-Sliced Correspondence Analysis Visualization. This novel method visualizes relationships through time and is suitable for large-scale temporal multivariate data. We couple CatViz with clustering methods, whereupon we introduce the concept of final centroid transfer, which enables the correspondence of clusters in time. Although CatViz can be used on any type of temporal data, we show how it can be applied to the task of exploratory visual analysis of text collections. We present a successful concept of employing feature-type filtering to present different aspects of textual data. We performed case studies on large collections of French and English news articles. In addition, we conducted a user study that confirms the usefulness of our method. We present typical tasks of exploratory text analysis and discuss application procedures that an analyst might perform. We believe that CatViz is general and highly applicable to large data sets because of its intuitiveness, effectiveness, and robustness. We expect that it will enable a better understanding of texts in huge historical archives.

Résumé: The need for competence-based approach in professional and educational standards is required by modern economy. Development and improvement of systems of different competences provides a link between education and training and labor market. The discussion on the current state and prospects of development of joint master’s program in Business Informatics is being held. Hereby there is a suggestion on competencies description for Business Informatics Curriculum based on descriptors. To realize the competence oriented model for master’s program it is suggested to mark out educational concepts trough the type of courses in each discipline.

Commentaires: reiso10svc

Ref HAL: hal-00516045_v1
Résumé: Organiser les données textuelles et en tirer du sens est un défi majeur aujourd’hui. Ainsi, lorsque l’on souhaite analyser un débat en ligne ou un forum de discussion, on voudrait pouvoir rapidement voir quels sont les principaux thèmes abordés et la manière dont la discussion se structure autour d’eux. Pour cela, et parce que un même texte peut être associé à plusieurs thèmes, nous proposons une méthode originale pour regrouper les données textuelles en autorisant les chevauchements et pour nommer chaque groupe de manière lisible. La contribution principale de cet article est une méthode globale qui permet de réaliser toute la chaîne, partant des données textuelles brutes jusqu’à la caractérisation des groupes à un niveau sémantique qui dépasse le simple ensemble de mots.

Commentaires: egc10rvc

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Ref HAL: hal-00555014_v1
Résumé: Data warehouses are the core of decision support systems, which nowadays are used by all kind of enterprises in the entire world. Although many studies have been conducted on the need of decision support systems (DSSs) for small businesses, most of them adopt existing solutions and approaches, which are appropriate for large-scaled enterprises, but are inadequate for small and middle-sized enterprises. Small enterprises require cheap, lightweight architectures and tools (hardware and software) providing online data analysis. In order to ensure these features, we review web-based business intelligence approaches. For real-time analysis, the traditional OLAP architecture is heavy and storage-costly; therefore, we also review in-memory processing. Consequently, this paper discusses the existing approaches and tools working in main memory and/or with web interfaces (including freeware tools), particularly aimed at aiding small and middle-sized enterprises in decision making.