7,128 research outputs found

    A Task Taxonomy for Temporal Graph Visualisation

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    By extending and instantiating an existing formal task framework, we define a task taxonomy and task design space for temporal graph visualisation. We discuss the process involved in their generation, and describe how the design space can be ā€˜sliced and dicedā€™ into multiple overlapping task categories, requiring distinct visual techniques for their support. The approach addresses deficiencies in the task literature, offering domain independence, greater task coverage, and unambiguous task specification. The taxonomy and design space capture tasks for temporal graphs, and also static graphs, multivariate graphs, and graph comparison, and will be of value in the design and evaluation of temporal graph visualisation systems

    Tasks and visual techniques for the exploration of temporal graph data

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    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

    Evaluation of two interaction techniques for visualization of dynamic graphs

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    Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

    Visualisation of Long in Time Dynamic Networks on Large Touch Displays

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    Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging ļ¬elds from travel networks to epidemiology.However, it is very difļ¬cult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We ļ¬rst introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ā€˜interactive timeslicingā€™. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To conļ¬rm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we ļ¬nd that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also ļ¬nd that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the ļ¬rst, but this time using standard-sized displays with indirect mouse input. The second study reafļ¬rms the results of the ļ¬rst, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types

    Designing multi-sensory displays for abstract data

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    The rapid increase in available information has lead to many attempts to automatically locate patterns in large, abstract, multi-attributed information spaces. These techniques are often called data mining and have met with varying degrees of success. An alternative approach to automatic pattern detection is to keep the user in the exploration loop by developing displays for perceptual data mining. This approach allows a domain expert to search the data for useful relationships and can be effective when automated rules are hard to define. However, designing models of the abstract data and defining appropriate displays are critical tasks in building a useful system. Designing displays of abstract data is especially difficult when multi-sensory interaction is considered. New technology, such as Virtual Environments, enables such multi-sensory interaction. For example, interfaces can be designed that immerse the user in a 3D space and provide visual, auditory and haptic (tactile) feedback. It has been a goal of Virtual Environments to use multi-sensory interaction in an attempt to increase the human-to-computer bandwidth. This approach may assist the user to understand large information spaces and find patterns in them. However, while the motivation is simple enough, actually designing appropriate mappings between the abstract information and the human sensory channels is quite difficult. Designing intuitive multi-sensory displays of abstract data is complex and needs to carefully consider human perceptual capabilities, yet we interact with the real world everyday in a multi-sensory way. Metaphors can describe mappings between the natural world and an abstract information space. This thesis develops a division of the multi-sensory design space called the MS-Taxonomy. The MS-Taxonomy provides a concept map of the design space based on temporal, spatial and direct metaphors. The detailed concepts within the taxonomy allow for discussion of low level design issues. Furthermore the concepts abstract to higher levels, allowing general design issues to be compared and discussed across the different senses. The MS-Taxonomy provides a categorisation of multi-sensory design options. However, to design effective multi-sensory displays requires more than a thorough understanding of design options. It is also useful to have guidelines to follow, and a process to describe the design steps. This thesis uses the structure of the MS-Taxonomy to develop the MS-Guidelines and the MS-Process. The MS-Guidelines capture design recommendations and the problems associated with different design choices. The MS-Process integrates the MS-Guidelines into a methodology for developing and evaluating multi-sensory displays. A detailed case study is used to validate the MS-Taxonomy, the MS-Guidelines and the MS-Process. The case study explores the design of multi-sensory displays within a domain where users wish to explore abstract data for patterns. This area is called Technical Analysis and involves the interpretation of patterns in stock market data. Following the MS-Process and using the MS-Guidelines some new multi-sensory displays are designed for pattern detection in stock market data. The outcome from the case study includes some novel haptic-visual and auditory-visual designs that are prototyped and evaluated
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