Abductive sensemaking through sketching:A categorization of the dimensions in sketching capacities in design

This paper proposes design sketching as a way to make abductive reasoning manifest and concrete. Through sketching, the abductive sensemaking leaves the domain of abstract logics and becomes part of the researchers or practitioner’s reflective practice. This practice is especially evident through incorporating sketching as more than a specific technique, but also as ways of applying design thinking through acting upon the world. The paper presents sketching as an integral part of the design epistemology. Furthermore, a categorization of different dimensions in which sketching can be represented is presented. The main contribution is a discussion of whether this broader view on sketching capacities in design leaves room for further exploration into extended sketching capacities for design

Abductive sensemaking through sketching: A categorization of the dimensions in sketching capacities in design

This paper proposes design sketching as a way to make abductive reasoning manifest and concrete. Through sketching, the abductive sensemaking leaves the domain of abstract logics and becomes part of the researchers or practitioner’s reflective practice. This practice is especially evident through incorporating sketching as more than a specific technique, but also as ways of applying design thinking through acting upon the world. The paper presents sketching as an integral part of the design epistemology. Furthermore, a categorization of different dimensions in which sketching can be represented is presented. The main contribution is a discussion of whether this broader view on sketching capacities in design leaves room for further exploration into extended sketching capacities for design

Abductive sensemaking through sketching: A categorization of the dimensions in sketching capacities in design

This paper proposes design sketching as a way to make abductive reasoning manifest and concrete. Through sketching, the abductive sensemaking leaves the domain of abstract logics and becomes part of the researchers or practitioner’s reflective practice. This practice is especially evident through incorporating sketching as more than a specific technique, but also as ways of applying design thinking through acting upon the world. The paper presents sketching as an integral part of the design epistemology. Furthermore, a categorization of different dimensions in which sketching can be represented is presented. The main contribution is a discussion of whether this broader view on sketching capacities in design leaves room for further exploration into extended sketching capacities for design

A Potential-Field-Based Multilevel Algorithm for Drawing Large Graphs

The aim of automatic graph drawing is to compute a well-readable layout of a given graph G=(V,E). One very popular class of algorithms for drawing general graphs are force-directed methods. These methods generate drawings of G in the plane so that each edge is represented by a straight line connecting its two adjacent nodes. The computation of the drawings is based on associating G with a physical model. Then, the algorithms iteratively try to find a placement of the nodes so that the total energy of the physical system is minimal. Several force-directed methods can visualize large graphs containing many thousands of vertices in reasonable time. However, only some of these methods guarantee a sub-quadratic running time in special cases or under certain assumptions, but not in general. The others are not sub-quadratic at all. We develop a new force-directed algorithm that is based on a combination of an efficient multilevel strategy and a method for approximating the repulsive forces in the system by rapidly evaluating potential fields. The worst-case running time of the new method is O(|V| log|V|+|E|) with linear memory requirements. In practice, the algorithm generates nice drawings of graphs containing up to 100000 nodes in less than five minutes. Furthermore, it clearly visualizes even the structures of those graphs that turned out to be challenging for other tested methods

Visualisation interactive de grands volumes de données incertaines (pour une approche perceptive)

Les études scientifiques et d'ingénierie actuelles font de plus en plus souvent appel à des techniques de simulation numérique pour étudier des phénomènes physiques complexes. La visualisation du résultat de ces simulations sur leur support spatial, souvent nécessaire à leur bonne compréhension, demande la mise en place d'outils adaptés, permettant une restitution fidèle et complète de l'information présente dans un jeu de données. Une telle visualisation doit donc prendre en compte les informations disponibles sur la qualité du jeu de données et l'incertitude présente. Cette thèse a pour but d'améliorer les méthodes de visualisation des champs de données scalaires de façon à intégrer une telle information d'incertitude. Les travaux présentés adoptent une approche perceptive, et utilisent les méthodes expérimentales et les connaissances préalables obtenues par la recherche sur la perception visuelle pour proposer, étudier et finalement mettre en oeuvre des nouvelles techniques de visualisation. Une revue de l'état de l'art sur la visualisation de données incertaines nous fait envisager l'utilisation d'un bruit procédural animé comme primitive pour la représentation de l'incertitude. Une expérience de psychophysique nous permet d'évaluer des seuils de sensibilité au contraste pour des stimuli de luminance générés par l'algorithme de bruit de Perlin, et de déterminer ainsi dans quelles conditions ces stimuli seront perçus. Ces résultats sont validés et étendus par l'utilisation d'un modèle computationnel de sensibilité au contraste, que nous avons réimplémenté et exécuté sur nos stimuli. Les informations obtenues nous permettent de proposer une technique de visualisation des données scalaires incertaines utilisant un bruit procédural animé et des échelles de couleur, intuitive et efficace même sur des géométries tridimensionnelles complexes. Cette technique est appliquée à deux jeux de données industriels, et présentée à des utilisateurs experts. Les commentaires de ces utilisateurs confirment l'efficacité et l'intérêt de notre technique et nous permettent de lui apporter quelques améliorations, ainsi que d'envisager des axes de recherche pour des travaux futurs.Current scientific and engineering works make an increasingly frequent use of numerical simulation techniques to study complex physical phenomenons. Visualizing these simulations' results on their geometric structure is often necessary in order to understand and analyze the simulated system. Such a visualization requires specific software tools in order to achieve a comprehensive and accurate depiction of the information present in the dataset. This includes taking into account the available information about dataset quality and data uncertainty. The goal of this thesis is to improve the visualization techniques for scalar data fields in order to integrate uncertainty information to the result. Our work follows a perceptual approach, using knowledge and experimental methods from visual perception research to put forward, study and implement new visualization techniques. A review of the state of the art on uncertainty visualization make us suggest to use an animated procedural noise as a visual primitive to show uncertainty. We set up a psychophysics experiment to evaluate contrast sensitivity thresholds for luminance stimuli generated using Perlin's noise algorithm, and therefore understand under which conditions such noise patterns can be perceived. These results are validated and extended by using a computational model of contrast sensitiviy, which we reimplemented and ran on our stimuli. The resulting information allow us to put forward a new technique for visualizing uncertain scalar data using an animated procedural noise and color maps. The resulting visualization is intuitive and efficient even for datasets with a complex tridimensional geometry. We apply this new technique to two industrial datasets, and demonstrate it to expert users. Their feedback uphold the usabiliy and efficiency of our technique, and allows us to add a few more improvements and to orient our future work.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

A sketching-oriented design method for information visualization software.

The aim of this research is to describe a useful approach for supporting creativity and problem-solving in the design of Information Visualization software. This type of software is useful for helping people to understand large or complex collections of data by making the data easier to see and use. Because it can be so helpful, many people are motivated to create visualization software to address their own unique problems of understanding data. However, the techniques which visualizations use to enhance cognition of data are not widely known. Also, there are currently few resources which comprehensively describe a method for designing novel visualizations. Consequently, people who seek to build new Information Visualization tools are left to consult design examples, guidelines, and reference models, which do not adequately describe the visualization design process. The key question of the research concerns how Information Visualization methodologies should account for representation of the user, existing visualization design knowledge, and sketching. Given that the current methods of Information Visualization design are incomplete and show evidence of significant shortcomings, how can novice visualization design teams bridge these gaps by using methods from other design disciplines to successfully create effective visualizations To investigate this question, several studies were conducted. Also, a design methodology called So Viz was developed. It incorporates a participatory design approach, using sketching and visualization design patterns to support creativity and problem-solving. A prototype was designed using the SoViz approach. The key contributions of this thesis are results which show that Information Visualization designers can benefit from using this method. The thesis presents the results of using SoViz to create an Information Visualization prototype and describes the theoretical consequences for Information Visualization methodology

A Potential-Field-Based Multilevel Algorithm for Drawing Large Graphs

The aim of automatic graph drawing is to compute a well-readable layout of a given graph G=(V,E). One very popular class of algorithms for drawing general graphs are force-directed methods. These methods generate drawings of G in the plane so that each edge is represented by a straight line connecting its two adjacent nodes. The computation of the drawings is based on associating G with a physical model. Then, the algorithms iteratively try to find a placement of the nodes so that the total energy of the physical system is minimal. Several force-directed methods can visualize large graphs containing many thousands of vertices in reasonable time. However, only some of these methods guarantee a sub-quadratic running time in special cases or under certain assumptions, but not in general. The others are not sub-quadratic at all. We develop a new force-directed algorithm that is based on a combination of an efficient multilevel strategy and a method for approximating the repulsive forces in the system by rapidly evaluating potential fields. The worst-case running time of the new method is O(|V| log|V|+|E|) with linear memory requirements. In practice, the algorithm generates nice drawings of graphs containing up to 100000 nodes in less than five minutes. Furthermore, it clearly visualizes even the structures of those graphs that turned out to be challenging for other tested methods