Metodologías para el desarrollo de interfaces visuales de recuperación de información : análisis y comparación

Introduction. In recent years the volume of electronic information has grown exponentially. This phenomenon improves data exchange and communication but introduces new troubles in relation to information access and searching. Aim. This paper proposes an exhaustive review of the different models, methods and algorithms that can be used to develop Visual Interfaces for Information Retrieval. The methods are classified on the basis of the stage of the process in which they take part: data analysis and transformation, application of classification and visual distribution algorithms, and application of visual transformation techniques. Methodology. Based on the analysis, we compare the different methods that can be used in each stage of the production process. We also determine which combinations of methods and algorithms are most suitable at different stages. Results. In the first section, data analysis and transformation, we analyse content mining, structure mining and use mining. In the second section, visual classification algorithm, we shown the hierarchical, network, scattering and map representations. In the last section, visual transformation techniques, we present the distortion (Focus+Context) and non-distortion techniques. Conclusion. The results aim to become useful tools for other researchers when choosing a methodological combination for the development of specific proposals for visual interfaces for information retrieval, as well as suggest implications to be considered on the research of new visual transformation techniques

Visualizing the evolution of a subject domain: a case study.

Visualization '99. Proceedings, pp.449-561. Retrieved October 29th, 2007 from http://www.pages.drexel.edu/~cc345/papers/infovis99.pdfWe explore the potential of information visualization techniques in enhancing existing methodologies for domain analysis and modeling. In this case study, we particularly focus on visualizing the evolution of the hypertext field based on author co-citation patterns, including the use of a sliding-window scheme to generate a series of annual snapshots of the domain structure, and a factor referenced color-coding scheme to highlight predominant specialties in the field

Exploring the Mobile Structural Assessment Tool: Concept Maps for Learning Website

In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.En este artículo se describe cómo el algoritmo de búsqueda de ruta convierte puntajes de conceptos pareados en mapas conceptuales. También se presenta cómo este algoritmo ha sido utilizado para desarrollar estos mapas conceptuales para aprendizaje (www.conceptmapsforlearning.com) basados en los principios del aseguramiento formativo efectivo. Las redes de búsqueda de ruta, una de las herramientas de representación de redes, ayudan a memorizar a los estudiantes y enunciar las relaciones entre mapas más que las herramientas de expresión espacial (tales como el escalonamiento multidimensional). Por tanto, las redes de búsqueda de rutas han sido usadas en varios estudios de estructura del conocimiento incluyendo la identificación de malos conceptos usados por los estudiantes. Para lograr esto, cada mapa de conocimiento tanto del estudiante como del experto son comparados vía el software de búsqueda de ruta y se remarcan las diferencias entre éstos. Después que los malos conceptos son identificados, el software de búsqueda falla en entregar una retroalimentación en estos nodos conceptuales. Para superar esta debilidad, se desarrolla una herramienta de mapa conceptual móvil que manda retroalimentaciones visuales, textuales y remediales (e.g. vídeos, enlaces a páginas web y applets) en las relaciones de los conceptos. Adicionalmente, los estudiantes son preguntados acerca de qué entienden de la retroalimentación brindada y se les da la oportunidad de revisar sus mapas de conocimiento después de recibir varios tipos de retroalimentación

Visualizing a knowledge domain's intellectual structure

Computer, 34 (3): pp. 65-71.To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape

Visualizing evolving networks: minimum spanning trees versus Pathfinder networks

Paper presented at the Proceedings of IEEE Symposium on Information Visualization, 2003, Seattle, Washington.Network evolution is a ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualization of network evolution. We compare the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and Pathfinder networks (PFNETs). Our primarily goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account

Interfaz visual para recuperación de información basada en análisis de metadatos, escalamiento multidimensional y efecto ojo de pez

This paper proposes an original model of the visual information retrieval interface (VIRI), aimed at websites described by metadata. The paper also describes the development of an experimental prototype based on the proposed model. The applied methodology for the prototype's development is a combination of three methods: Metadata mining, Multidimensional scaling (MDS), and a Focus+Context technique called Fisheye. Finally, a user test is conducted, whose results suggest that the proposed model of VIRI is useful for information browsing and retrieval, and is satisfying for the end-user as well