Author: Kai
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Guest editor for special issue on “Provenance analysis for sensemaking” ( IEEE Computer Graphics and Applications)
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.
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Provenance analysis for sensemaking
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
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Provenance and logging for sense making (Dagstuhl Reports)
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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An Approach to Human-Machine Teaming in Legal Investigations Using Anchored Narrative Visualisation and Machine Learning (International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace)
The First International Workshop on AI and Intelligent Assistance for Legal Professionals in the Digital Workplace (LegalAIIA 2019) During legal investigations, analysts typically create external representations of an investigated domain as resource for cognitive offloading, reflection and collaboration. For investigations involving very large numbers of documents as evidence, creating such representations can be slow and…
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Uncertainty of visualizations for SenseMaking in criminal intelligence analysis (EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization)
EuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3) 2018 Uncertainty in visualization is an inevitable issue for sensemaking in criminal intelligence. Accuracy and precision of adoptedvisualization techniques have got greater role in trustworthiness with the system while finding out insights from crime relateddataset. In this paper, we have presented a case study to…
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Combining Data Visualization with Machine Learning to Make Sense of Large Social Media Data (2018-2019, US$20,000)
Funder: Microsoft Azure Research Award Date: 2018-2019 Funding: US$20,000 In this project we utilise the Microsoft Azure to analyse over 1.5 billion tweets using an updated SAVI (Social Analytics VIsualisation) system.
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Organiser of the Dagstuhl Seminar on “Provenance and Logging for Sense Making”
Dagstuhl seminar is a series of Computer Science seminars hosted at the Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH in Germany. It is an invitation only seminar and a selected group of leading international experts on a certain topic spend a week in the Dagstuhl castle discussing the solutions to the most challenging research problems related…
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Analytic Provenance as Constructs of Behavioural Markers for Externalizing Thinking Processes in Criminal Intelligence Analysis (Community-Oriented Policing and Technological Innovations)
Chapter 10 of the book Community-Oriented Policing and Technological Innovations (2018) Studying how analysts use interaction in visualization systems is an important part of evaluating how well these interactions support analysis needs, like generating insights or performing tasks. Analytic Provenance commonly known as interaction histories contains information about the sequence of choices that analysts make…
Human-AI Collaboration 