Characterizing the intelligence analysis process through a longitudinal field study: Implications for visual analytics

Kang, Youn Ah; Stasko, John

doi:10.1177/1473871612468877

Cited by 12 publications

(11 citation statements)

References 21 publications

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“…Kang and Stasko recently demonstrated the importance of externalizing an analyst's thought process, of which making annotations is a key aspect [21]. They observed that analysts continuously build a conceptual model in the sensemaking process and suggested visual representations can be leveraged to help analysts browse the history of previous discoveries in order to "connect, group, and organize concepts."…”

Section: Related Workmentioning

confidence: 99%

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Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations

Zhao

Glueck

Breslav

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

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Section: Related Workmentioning

confidence: 99%

“…Facilitating annotations is an important aspect of supporting the knowledge externalization process [10,20,21,24]. Yet, commercial exploratory data analysis systems and visualizations that support annotations still present user-authored comments as lists; in a linear and textual form.…”

Section: Introductionmentioning

confidence: 99%

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations

Zhao

Glueck

Breslav

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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“…There exist a number of taxonomies for information visualisation tasks, both abstract [57,90] and more concrete [7,8,11,168,280], but we are primarily interested in taxonomies generated for network data. Due to the relative lack of work in this area, we do not further isolate the discussion to only taxonomies of dynamic network data.…”

Section: Visualisation Task Taxonomiesmentioning

confidence: 99%

Visualisation of Long in Time Dynamic Networks on Large Touch Displays

Lee¹

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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 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.

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“…Public health intelligence research is: "the concepts, methods, practices, and apparatuses assembled to monitor and detect health events" (French & Mykhalovskiy, 2013). Indeed, there has not been much research on any kind of intelligence analysis during a crisis, not only a public health crisis (Hutchins, Pirolli, & Card, 2007;Kang & Stasko, 2014).…”

Section: Information Issuesmentioning

confidence: 99%

“…For example, there is strong body of research on coordination of multiple agencies in crisis response (Bharosa et al, 2010); but we also need more research for crisis management longitudinally and being able to map events and decisions from months or even years ago to the current day, for a long duration crisis. This also has applications into information systems design (Kang & Stasko, 2014) for intelligence analysis and coordination during a long duration crisis.…”

Section: Recommendations For Future Researchmentioning

confidence: 99%

Expert Advisory Groups: Exploring the Sensemaking Process During a Public Health Crisis Response

Seto¹

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<p>Crisis sensemaking research has focused mainly on acute crises such as wildfires or industrial accidents, with crisis response being approximately under 72 hours. However, there is limited research on long duration crisis sensemaking for crisis response that may be several weeks, months, or even years. This research study aims to explore long duration crisis sensemaking during a public health crisis. During the crisis response period, key decision makers (KDMs) face a plethora of challenges, including being inundated with information, with varying levels of quality and relevance, or not having the right kind of information. They may rely on an Expert Advisory Group (EAG) to advise on the scientific/medical aspect of the disease. The EAG is comprised of specialists such as infectious disease physicians, infection prevention and control practitioners, epidemiologists, and public health physicians. The 2003 SARS outbreak in Toronto, Canada, was the context for this research. Participants were recruited who served as members of the Ontario SARS Scientific Advisory Committee (OSSAC) or were stakeholders during the crisis. Among their duties, these experts were tasked to write directives (mandated protocols) that govern all aspects of hospital life, from patient transfers, to cleaning. Data was collected in multiple forms, including: public inquiry reports, meeting minutes, newspaper articles, and interviews. Following a constructivist grounded theory strategy, I conducted several iterations of data collection and analysis. The findings include a conceptual framework of EAG social sensemaking through a long duration crisis, depicting the sequential process of a stream of sensemaking (the creation and revision of one directive). A second conceptual framework on the information dynamics of long duration social sensemaking reflects the learning over the course of the crisis period. Finally, a third conceptual framework on the regulation of expert advisory group sensemaking as a balance between the knowns and unknowns in the greater health system is presented.</p>

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Characterizing the intelligence analysis process through a longitudinal field study: Implications for visual analytics

Cited by 12 publications

References 21 publications

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations

Visualisation of Long in Time Dynamic Networks on Large Touch Displays

Expert Advisory Groups: Exploring the Sensemaking Process During a Public Health Crisis Response

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