Analytic Provenance for Sensemaking: A Research Agenda (CG&A 2015)

This is the outcomes from the analytic provenance workshop organised at VIS 2014. It provides an overview of the research related to analytic provenance and the challenges/open problems it faces (a lot). PDF (IEEE digital library Xplore)

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Open-Source Big Data Insight (2014-2015, £80k)

A project to build the Big Data infrastructure /Data Lake for the visual analysis of open-source intelligence data.

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Concern Level Assessment: Building Domain Knowledge into a Visual System to Support Network Security Situation Awareness (Information Visualisation Journal 2014)

This is a detailed report of our winner entry to the VAST challenge 2012.

Provenance for Sensemaking Workshop at IEEE VIS 2014 (Paris, France)

I am co-chairing a workshop on Provenance and Sensemaking at the IEEE VIS conference week in Paris. The submission deadline has now passed, but you can still join the discussions at the workshop during the conference. Hope to see you there!

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High-Dimensional Visualisation for Big Data (2014-2015, £20k)

A data visualisation project to understand how interactive projection technique can help user improve their understanding of data with very high dimensionality.

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VAST Visual Analytics Challenge 2014 – Honorable Mention

This year, the Visual Analytics challenge organised by the IEEE VAST (Visual Analytics Science & Technology) conference again had a mini-challenge on Twitter data, and it is (near) real-time this time. The task is to monitor the Twitter feed and provide (near) real-time updates on important events that are happening and find information related to a suspected kidnapping.

Our entry won a ‘Honorable Mention for Effective Support for Analytic Sensemaking’, a step away from getting an award. You can see the system working in the demo video below (best in full screen with sound on).

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Book chapter on Multivariate Network Visualisation

T. J. Jankun-Kelly, T. Dwyer, D. Holten, C. Hurter, M. Nöllenburg, C. Weaver, and K. Xu, “Scalability Considerations for Multivariate Graph Visualization,” in Multivariate Network Visualization, A. Kerren, H. C. Purchase, and M. O. Ward, Eds. Springer International Publishing, 2014, pp. 207–235. PDF.
Multivariate network is a quite challenging problem: it involves both the network structure and the attributes of the nodes and edges. Think of a social network: you have people as the nodes and their relationships as edges; there are information about the people (age, gender, profession) and their relationships (friends/colleague, when it started, etc). Considering both will provide  a deeper look into the social network, but makes the visualisation more difficult at the same time. This book chapter mainly surveys human performance related issues to this type of visualisation. This is an example of my previous work on this topic:
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