SenseMap: Supporting browser-based online sensemaking through analytic provenance

Nguyêñ, Phong Q.; Xu, Kai; Bardill, Andy; Salman, Betul; Herd, Kate; Wong, B. L. William

doi:10.1109/vast.2016.7883515

Cited by 35 publications

(23 citation statements)

References 36 publications

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“…Besides manual bookmarking, automatic capture of analytic provenance [30] is also commonly used to construct stories. SenseMap [31] automatically captures and visualizes users' actions, enabling the users to curate relevant information pieces, organize them, and communicate the analytical findings. Gratzl et al [32] introduce an authoring tool that constructs a story based on provenance capture, adds text and drawing annotation, and plays back the story as it was originally performed.…”

Section: Storytelling In Visual Analyticsmentioning

confidence: 99%

“…HTVA [14] allows adding a simple overlay describing the visual encoding and data filtering. SenseMap [31] helps construct a story with multi-level semantics that can be flexibly presented to the audience with different backgrounds and needs.…”

Section: Storytelling In Visual Analyticsmentioning

confidence: 99%

“…The difference of our approach from the previous work is that we consider construction of stories not from complete visualizations [28] or their components [29] or analysis bookmarks [31] but from structured data representing findings extracted during the process of analysis. The visual representation for these data is chosen according to their structure.…”

Section: Storytelling In Visual Analyticsmentioning

confidence: 99%

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Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Chen

Andrienko

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques supporting the analysis. Findings and results of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audiences but also the information that needs to be presented. Analysis results may consist of multiple components, which may involve multiple heterogeneous facets. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, within which two main challenges lie: information complexity and display complexity. We address this problem by proposing a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organises story contents. Unlike previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. We focus on selecting, assembling and organizing findings for further presentation rather than on tracking analysis history and enabling dual (i.e., explorative and communicative) use of data displays. In story synthesis, findings are selected, assembled, and arranged in meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed conceptual framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two diverse domains, social media and movement analysis.

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Section: Storytelling In Visual Analyticsmentioning

confidence: 99%

Section: Storytelling In Visual Analyticsmentioning

confidence: 99%

Section: Storytelling In Visual Analyticsmentioning

confidence: 99%

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Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Chen

Andrienko

et al. 2020

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…Besides application logs [6], event sequence data in many other domains such as web clickstreams [7], health records [8] and analytic provenance [9], [10] have been studied. For generality, we use sequence of events instead of session of actions to denote ordered lists of events.…”

Section: Related Workmentioning

confidence: 99%

Understanding User Behaviour through Action Sequences: From the Usual to the Unusual

Nguyêñ

Turkay

Andrienko

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Action sequences, where atomic user actions are represented in a labelled, timestamped form, are becoming a fundamental data asset in the inspection and monitoring of user behaviour in digital systems. Although the analysis of such sequences is highly critical to the investigation of activities in cyber security applications, existing solutions fail to provide a comprehensive understanding due to the complex semantic and temporal characteristics of these data. This paper presents a visual analytics approach that aims to facilitate a user-involved, multi-faceted decision making process during the identification and the investigation of unusual action sequences. We firstly report the results of the task analysis and domain characterisation process. Then we describe the components of our multi-level analysis approach that comprises of constraint-based sequential pattern mining and semantic distance based clustering, and multi-scalar visualisations of users and their sequences. Finally, we demonstrate the applicability of our approach through a case study that involves tasks requiring effective decision-making by a group of domain experts. Although our solution here is tightly informed by a user-centred, domain-focused design process, we present findings and techniques that are transferable to other applications where the analysis of such sequences is of interest.

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“…However, the Computer Human Interface (CHI)/Human Computer Interaction (HCI) communities have been developing new tools to support complex search tasks since the 1970s. By engaging rich user interactions, new designs such as SenseMap [ 12 ] and Apolo [ 13 ] can generate rich form search logs with much more information than is contained in plain search logs. Thus, new methods should be studied to extract subtasks from these rich form search logs generated by modern complex search tools for better subtask-oriented query recommendations.…”

Section: Introductionmentioning

confidence: 99%

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

Zhao

Zhang

et al. 2018

Entropy

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Since complex search tasks are usually divided into subtasks, providing subtask-oriented query recommendations is an effective way to support complex search tasks. Currently, most subtask-oriented query recommendation methods extract subtasks from plain form search logs consisting of only queries and clicks, providing limited clues to identify subtasks. Meanwhile, for several decades, the Computer Human Interface (CHI)/Human Computer Interaction (HCI) communities have been working on new complex search tools for the purpose of supporting rich user interactions beyond just queries and clicks, and thus providing rich form search logs with more clues for subtask identification. In this paper, we researched the provision of subtask-oriented query recommendations by extracting thematic experiences from the rich form search logs of complex search tasks logged in a proposed visual data structure. We introduce the tree structure of the visual data structure and propose a visual-based subtask identification method based on the visual data structure. We then introduce a personalized PageRank-based method to recommend queries by ranking nodes on the network from the identified subtasks. We evaluated the proposed methods in experiments consisting of informative and tentative search tasks.

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SenseMap: Supporting browser-based online sensemaking through analytic provenance

Cited by 35 publications

References 36 publications

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Understanding User Behaviour through Action Sequences: From the Usual to the Unusual

Recommending Queries by Extracting Thematic Experiences from Complex Search Tasks

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