5,322 research outputs found
Doctor of Philosophy
dissertationThis dissertation establishes a new visualization design process model devised to guide visualization designers in building more effective and useful visualization systems and tools. The novelty of this framework includes its flexibility for iteration, actionability for guiding visualization designers with concrete steps, concise yet methodical definitions, and connections to other visualization design models commonly used in the field of data visualization. In summary, the design activity framework breaks down the visualization design process into a series of four design activities: understand, ideate, make, and deploy. For each activity, the framework prescribes a descriptive motivation, list of design methods, and expected visualization artifacts. To elucidate the framework, two case studies for visualization design illustrate these concepts, methods, and artifacts in real-world projects in the field of cybersecurity. For example, these projects employ user-centered design methods, such as personas and data sketches, which emphasize our teams' motivations and visualization artifacts with respect to the design activity framework. These case studies also serve as examples for novice visualization designers, and we hypothesized that the framework could serve as a pedagogical tool for teaching and guiding novices through their own design process to create a visualization tool. To externally evaluate the efficacy of this framework, we created worksheets for each design activity, outlining a series of concrete, tangible steps for novices. In order to validate the design worksheets, we conducted 13 student observations over the course of two months, received 32 online survey responses, and performed a qualitative analysis of 11 in-depth interviews. Students found the worksheets both useful and effective for framing the visualization design process. Next, by applying the design activity framework to technique-driven and evaluation-based research projects, we brainstormed possible extensions to the design model. Lastly, we examined implications of the design activity framework and present future work in this space. The visualization community is challenged to consider how to more effectively describe, capture, and communicate the complex, iterative nature of data visualization design throughout research, design, development, and deployment of visualization systems and tools
Exploratory Visualization of Data Pattern Changes in Multivariate Data Streams
More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it.
As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user\u27s interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques.
A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis.
A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time
TwitInfo: Aggregating and Visualizing Microblogs for Event Exploration
Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied
Does Court Type, Size and Employee Satisfaction Affect Court Speed?. Hierarchical Linear Modelling With Evidence from Kenya
In most judicial institutions, well-functioning courts are usually expected to process a large volume of work within demanding timelines. For courts to have played their role of enhancing access to justice, the yardstick of success is often viewed through the lens of the speed attained in rendering justice. In Kenya, despite the desirable timeline for finalizing most of the cases being ‘within 360 days’ from the date of case filing in courts, by the end of June 2020, 58 per cent of the unresolved cases had surpassed this timeline and subsequently classified as backlog. In the period 2018/19, the percentage of civil cases that were resolved within the set timeline by High Court and Magistrate Court, the two largest court types by volume of work, was 37 and 42 per cent respectively. Over the same period, the percentage of criminal cases that were resolved within the set timeline was 42 and 84 per cent for the two court types respectively. Evidently therefore, the Kenyan courts had not managed to resolve cases within the desirable timeline. To unearth the reasons that could be occasioning the delay, this study investigated the factors that were potentially affecting court speed. Specifically, the study set out to determine the variation in court speed attributable to court type, and further analyze the effect of court size and employee satisfaction on court speed. This was achieved through the use of Hierarchical Linear Modelling, cross sectional data for the period 2018/19 and estimation using Restricted Maximum Likelihood technique. The results revealed the existence of relatively high variation in court speed that is attributable to court type, and that the smaller the court size, the higher the court speed. Further, high level of employee satisfaction was found to increase timely resolution of cases. Consequently, diverse strategies and policy actions for enhancing court speed have been suggested. Keywords: Court Speed, Court Type, Court Size, Employee Satisfaction, Hierarchical Linear Modelling DOI: 10.7176/JLPG/110-02 Publication date:June 30th 202
Marker effects and examination reliability: a comparative exploration from the perspectives of generalizability theory, Rasch modelling and multilevel modelling
This study looked at how three different analysis methods could help us to understand rater effects on exam reliability. The techniques we looked at were: generalizability theory (G-theory) item response theory (IRT): in particular the Many-Facets Partial Credit Rasch Model (MFRM) multilevel modelling (MLM) We used data from AS component papers in geography and psychology for 2009, 2010 and 2011 from Edexcel.</p
Visualisation of Long in Time Dynamic Networks on Large Touch Displays
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 fields from travel networks to epidemiology.However, it is very difficult 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 first 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 ‘interactive timeslicing’. 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 confirm 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 find 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 find 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 first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, 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
Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology
Topological data analysis is an emerging area in exploratory data analysis
and data mining. Its main tool, persistent homology, has become a popular
technique to study the structure of complex, high-dimensional data. In this
paper, we propose a novel method using persistent homology to quantify
structural changes in time-varying graphs. Specifically, we transform each
instance of the time-varying graph into metric spaces, extract topological
features using persistent homology, and compare those features over time. We
provide a visualization that assists in time-varying graph exploration and
helps to identify patterns of behavior within the data. To validate our
approach, we conduct several case studies on real world data sets and show how
our method can find cyclic patterns, deviations from those patterns, and
one-time events in time-varying graphs. We also examine whether
persistence-based similarity measure as a graph metric satisfies a set of
well-established, desirable properties for graph metrics
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