Cognitive Activity Support Tools: Design of the Visual Interface

This dissertation is broadly concerned with interactive computational tools that support the performance of complex cognitive activities, examples of which are analytical reasoning, decision making, problem solving, sense making, forecasting, and learning. Examples of tools that support such activities are visualization-based tools in the areas of: education, information visualization, personal information management, statistics, and health informatics. Such tools enable access to information and data and, through interaction, enable a human-information discourse. In a more specific sense, this dissertation is concerned with the design of the visual interface of these tools. This dissertation presents a large and comprehensive theoretical framework to support research and design. Issues treated herein include interaction design and patterns of interaction for cognitive and epistemic support; analysis of the essential properties of interactive visual representations and their influences on cognitive and perceptual processes; an analysis of the structural components of interaction and how different operational forms of interaction components affect the performance of cognitive activities; an examination of how the information-processing load should be distributed between humans and tools during the performance of complex cognitive activities; and a categorization of common visualizations according to their structure and function, and a discussion of the cognitive utility of each category. This dissertation also includes a chapter that describes the design of a cognitive activity support tool, as guided by the theoretical contributions that comprise the rest of the dissertation. Those that may find this dissertation useful include researchers and practitioners in the areas of data and information visualization, visual analytics, medical and health informatics, data science, journalism, educational technology, and digital games

Thinking interactively with visualization

Interaction is becoming an integral part of using visualization for analysis. When interaction is tightly and appropriately coupled with visualization, it can transform the visualization from display- ing static imagery to assisting comprehensive analysis of data at all scales. In this relationship, a deeper understanding of the role of interaction, its effects, and how visualization relates to interaction is necessary for designing systems in which the two components complement each other. This thesis approaches interaction in visualization from three different perspectives. First, it considers the cost of maintaining interaction in manipulating visualization of large datasets. Namely, large datasets often require a simplification process for the visualization to maintain interactivity, and this thesis examines how simplification affects the resulting visualization. Secondly, example interactive visual analytical systems are presented to demonstrate how interactivity could be applied in visualization. Specifically, four fully developed systems for four distinct problem domains are discussed to determine the common role of interactivity in these visualizations that make the systems successful. Lastly, this thesis presents evidence that interactions are important for analytical tasks using visualizations. Interaction logs of financial analysts using a visualization were collected, coded, and examined to determine the amount of analysis strategies contained within the interaction logs. The finding supports the benefits of high interactivity in analytical tasks when using a visualization. The example visualizations used to support these three perspectives are diverse in their goals and features. However, they all share similar design guidelines and visualization principles. Based on their characteristics, this thesis groups these visualizations into urban visualization, visual analytical systems, and interaction capturing and discusses them separately in terms of lessons learned and future directions

Méthodes systémiques d'analyse des données de simulation de modèles de voies de signalisation cellulaire

Les réseaux de pétri, un outil de modélisation polyvalent -- Une approche systémique en biologie moléculaire : le génie à la rencontre de la biologie -- Démarche de l'ensemble du travail de recherche et organisation générale du document -- Functional abstraction and spectral representation to visualize the system dynamics and the information flux in a biochemical model -- Petri net-based visualization of signal transduction pathway simulations -- Petri net-based method for the analysis of the dynamics of signal propagation in signaling pathways

An Interactive Visualisation System for Engineering Design using Evolutionary Computing

This thesis describes a system designed to promote collaboration between the human and computer during engineering design tasks. Evolutionary algorithms (in particular the genetic algorithm) can find good solutions to engineering design problems in a small number of iterations, but a review of the interactive evolutionary computing literature reveals that users would benefit from understanding the design space and having the freedom to direct the search. The main objective of this research is to fulfil a dual requirement: the computer should generate data and analyse the design space to identify high performing regions in terms of the quality and robustness of solutions, while at the same time the user should be allowed to interact with the data and use their experience and the information provided to guide the search inside and outside regions already found. To achieve these goals a flexible user interface was developed that links and clarifies the research fields of evolutionary computing, interactive engineering design and multivariate visualisation. A number of accessible visualisation techniques were incorporated into the system. An innovative algorithm based on univariate kernel density estimation is introduced that quickly identifies the relevant clusters in the data from the point of view of the original design variables or a natural coordinate system such as the principal or independent components. The robustness of solutions inside a region can be investigated by novel use of 'negative' genetic algorithm search to find the worst case scenario. New high performance regions can be discovered in further runs of the evolutionary algorithm; penalty functions are used to avoid previously found regions. The clustering procedure was also successfully applied to multiobjective problems and used to force the genetic algorithm to find desired solutions in the trade-off between objectives. The system was evaluated by a small number of users who were asked to solve simulated engineering design scenarios by finding and comparing robust regions in artificial test functions. Empirical comparison with benchmark algorithms was inconclusive but it was shown that even a devoted hybrid algorithm needs help to solve a design task. A critical analysis of the feedback and results suggested modifications to the clustering algorithm and a more practical way to evaluate the robustness of solutions. The system was also shown to experienced engineers working on their real world problems, new solutions were found in pertinent regions of objective space; links to the artefact aided comparison of results. It was confirmed that in practice a lot of design knowledge is encoded into design problems but experienced engineers use subjective knowledge of the problem to make decisions and evaluate the robustness of solutions. So the full potential of the system was seen in its ability to support decision making by supplying a diverse range of alternative design options, thereby enabling knowledge discovery in a wide-ranging number of applications

Tasks and visual techniques for the exploration of temporal graph data

This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same.The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way.While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated.Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques.The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified.The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems

Interactive optimization for supporting multicriteria decisions in urban and energy system planning

Climate change and growing urban populations are increasingly putting pressure on cities to reduce their carbon emissions and transition towards efficient and renewable energy systems. This challenges in particular urban planners, who are expected to integrate technical energy aspects and balance them with the conflicting and often elusive needs of other urban actors. This thesis explores how multicriteria decision analysis, and in particular multiobjective optimization techniques, can support this task. While multiobjective optimization is particularly suited for generating efficient and original alternatives, it presents two shortcomings when targeted at large, intractable problems. First, the problem size prevents a complete identification of all solutions. Second, the preferences required to narrow the problem size are difficult to know and formulate precisely before seeing the possible alternatives. Interactive optimization addresses both of these gaps by involving the human decision-maker in the calculation process, incorporating their preferences at the same time as the generated alternatives enrich their understanding of acceptable tradeoffs and important criteria. For interactive optimization methods to be adopted in practice, computational frameworks are required, which can handle and visualize many objectives simultaneously, provide optimal solutions quickly and representatively, all while remaining simple and intuitive to use and understand by practitioners. Accordingly, the main objective of this thesis is: To develop a decision support methodology which enables the integration of energy issues in the early stages of urban planning. The proposed response and main contribution is SAGESSE (Systematic Analysis, Generation, Exploration, Steering and Synthesis Experience), an interactive multiobjective optimization decision support methodology, which addresses the practical and technical shortcomings above. Its innovative aspect resides in the combination of (i) parallel coordinates as a means to simultaneously explore and steer the alternative-generation process, (ii) a quasi-random sampling technique to efficiently explore the solution space in areas specified by the decision maker, and (iii) the integration of multiattribute decision analysis, cluster analysis and linked data visualization techniques to facilitate the interpretation of the Pareto front in real-time. Developed in collaboration with urban and energy planning practitioners, the methodology was applied to two Swiss urban planning case-studies: one greenfield project, in which all buildings and energy technologies are conceived ex nihilo, and one brownfield project, in which an existing urban neighborhood is redeveloped. These applications led to the progressive development of computational methods based on mathematical programming and data modeling (in the context of another thesis) which, applied with SAGESSE, form the planning support system URBio. Results indicate that the methodology is effective in exploring hundreds of plans and revealing tradeoffs and synergies between multiple objectives. The concrete outcomes of the calculations provide inputs for specifying political targets and deriving urban master plans