IEEE Transaction on Visualisation and Computer Graphics (TVCG) Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manual processes such as interviews and the analysis of observational data. While it is technically possible to capture a history of user interactions and application states, it remains difficult […]
Epidemics (journal) This paper describes the collaborative efforts from the RAMPVIS consortium to help the epidemiologists modelling the various aspects of Covid19, including the spread of the disease and the impact of different isolation policies. Started as a voluntary effort, there are many challenges faced by the group of close to 20 visualisation experts, including […]
Funder: Defence Science and Technology Laboratory (Dstl), UK Date: 2019 Funding: £80,000 Partner: MASS Ltd In this project, we use the human-machine teaming approach to design a new visual analytics system that supports kill chain elicitation: The system allows user to mark up text in intelligence reports that may form part of a kill chain; […]
EuroVis/Computer Graphics Forum 2020 There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete […]
IEEE Computer Graphics and Applications (CG&A) 2020 TimeSets is a temporal data visualization technique designed to reveal insights into event sets, such as all the events linked to one person or organization. In this paper we describe two TimeSets-based visual analytics tools for intelligence analysis. In the first case, TimeSets is integrated with other visual […]
IEEE Computer Graphics and Applications (CG&A) 2020 Interactive data exploration and analysis is an inherently personal process. One’s background, experience, interests, cognitive style, personality, and other sociotechnical factors often shape such a process, as well as the provenance of exploring, analyzing, and interpreting data. This viewpoint posits both what personal information and how such personal […]
I am co-chairing the Machine Learning from User Interactions for Visualization and Analytics (MLUI) workshop this year. It is part of VisWeek 2020, which is the leading international conference on Visualisation (computer graphics, data visualisation, and visual analytics). The workshop is completely online this year, and you can find all the information on its website.
This special issue covers research that analyses provenance to better understand how users do sensemaking (information triage, foraging, reasoning, and hypothesis forming and testing). The detailed scope can be found in the Call for Paper. The special issue is now published as part of this issue of IEEE Computer Graphics and Application.
IEEE Computer Graphics and Applications 2020 This is the introduction I wrote (with the other co-editors) for the special issue on Provenance Analysis for Sensemaking in the IEEE Computer Graphics and Applications. This special issue reports the outcomes from the Dagstuhl Seminar I co-organised a year earlier on the same topic. https://ieeexplore.ieee.org/document/8889811
Dagstuhl Reports 2019 This the report for the Dagstuhl Seminar I co-organised. Sense making is one of the biggest challenges in data analysis faced by both the industry and the research community. It involves understanding the data and uncovering its model, generating a hypothesis, selecting analysis methods, creating novel solutions, designing evaluation, and also critical […]
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