Analysts can make better informed decisions with “Explainable AI”. Visualizations are often used to understand, diagnose, and refine AI models. Yet, it is unclear what type of interactions are appropriate for a given model and how the visualizations are perceived. Furthering research into sensemaking for visual analytics may be useful in understanding how user’s are interacting with visualizations for AI and in developing a naturalistic model of explanation. Conventional approaches consist of human experts applying theoretical sensemaking models to identify changes in information processing or utilizing recorded rationale provided by the users. However, these approaches can be inefficient and inaccurate since they heavily rely on subjective human reports. In this research, we aim to understand how data-driven techniques can automatically identify changes in user behavior (inflection points) based on user interaction logs collected from eye tracking and mouse interactions. We relay the results of a supervised classification system using Hidden Markov Models to predict changes in a visual data analysis of a cyber security scenario. Preliminary results indicate a 70% accuracy in identifying inflection points. These preliminary results suggest the feasibility of data-driven approaches furthering our understanding of sensemaking processes and interaction provenance.

/pdf/detecting-changes-in-user-behavior-to-understand-interaction-3lo989fkx4.pdf

Detecting Changes in User Behavior to Understand Interaction Provenance during Visual Data Analysis.

Visualization tools can take advantage of multiple coordinated views to support analysis of large, multidimensional data sets. Effective design of such views and layouts can be challenging, but understanding users’ analysis strategies can inform design improvements. We outline an approach for intelligent design configuration of visualization tools with multiple coordinated views, and we discuss a proposed software framework to support the approach. The proposed software framework could capture and learn from user interaction data to automate new compositions of views and widgets. Such a framework could reduce the time needed for meta analysis of the visualization use and lead to more effective visualization design.

Leveraging Interaction History for Intelligent Configuration of Multiple Coordinated Views in Visualization Tools

In exploratory data analysis, domain knowledge and experience play a central role in order to extract information from the data and to derive proof and knowledge. However, experienced domain experts are rarely the same people who carry out the data analyses. Therefore, utilizing domain expertise for guidance in analytic processes is a complex challenge. In recent years, machine learning has seen great advances. Increasing processing power and growth in data as well as affordable storage have led to more advanced algorithms. Therefore, with the emergence of applicable machine learning algorithms, there is now a method for preserving and making use even of complex knowledge. In this paper, we present a concept that allows to extract and utilize domain knowledge for exploratory data analysis. We introduce concepts of interaction store and analysis context store to record user interaction and context during an exploratory analysis. We use the recorded data to construct semantic interaction sequences and predict their potential insight. The prediction can then be used to guide other data scientist in their sensemaking while performing exploratory data analysis in similar domains and use cases. Furthermore, we discuss possible research opportunities and implications resulting from the presented concept.

Towards Utilizing Domain Expertise for Exploratory Data Analysis

Communicating one's sensemaking during a complex analysis session to explain thought processes is hard, yet most intelligence occurs in collaborative settings. Team members require a deeper understanding of the work being completed by their peers and subordinates, but little research has fully articulated best practices for analytic provenance consumers. This work proposes an automatic summarization technique that separates an analysis session and summarizes interaction provenance as textual blurbs to allow for meta-analysis of work done. Focusing on the domain of intelligence analysis, we demonstrate our segmentation technique using five datasets, including both publicly available and classified interaction logs. We shared our demonstration with a notoriously inaccessible population of expert reviewers with experience as United States Department of Defense analysts. Our findings indicate that the proposed pipeline effectively generates cards that display key events from interaction logs, facilitating the sharing of analysis progress. Yet, we also hear that there is a need for more prominent justifications and pattern elicitation controls to communicate analysis summaries more effectively. The expert review highlights the potential of automated approaches in addressing the challenges of provenance information in complex domains. We'd like to emphasize the need for further research into provenance communication in other domains. A free copy of this paper and all supplemental materials are available at https://osf.io/j4bxt 

From Data Dump to Digestible Chunks: Automated Segmentation and
  Summarization of Provenance Logs for Communication

The different characteristics of human performance in selecting receding and approaching targets by rotating the head in a 3D virtual environment

We present an analytic provenance data repository that can be used to study human analysis activity, thought processes, and software interaction with visual analysis tools during exploratory data analysis. We conducted a series of user studies involving exploratory data analysis scenario with textual and cyber security data. Interactions logs, think-alouds, videos and all coded data in this study are available online for research purposes. Analysis sessions are segmented in multiple sub-task steps based on user think-alouds, video and audios captured during the studies. These analytic provenance datasets can be used for research involving tools and techniques for analyzing interaction logs and analysis history. By providing high-quality coded data along with interaction logs, it is possible to compare algorithmic data processing techniques to the ground-truth records of analysis history.

Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs

In many types of dynamic interactive visualizations, it is often desired to interact with moving objects. Stopping moving objects can make selection easier, but pausing animated content can disrupt perception and understanding of the visualization. To address such problems, we explore selection techniques that only pause a subset of all moving targets in the visualization. We present various designs for controlling pause regions based on cursor trajectory or cursor position. We then report a dual-task experiment that evaluates how different techniques affect both target selection performance and contextual awareness of the visualization. Our findings indicate that all pause techniques significantly improved selection performance as compared to the baseline method without pause, but the results also show that pausing the entire visualization can interfere with contextual awareness. However, the problem with reduced contextual awareness was not observed with our new techniques that only pause a limited region of the visualization. Thus, our research provides evidence that region-limited pause techniques can retain the advantages of selection in dynamic visualizations without imposing a negative effect on contextual awareness.

Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations

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Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs

Detecting Changes in User Behavior to Understand Interaction Provenance during Visual Data Analysis.

Leveraging Interaction History for Intelligent Configuration of Multiple Coordinated Views in Visualization Tools

Preserving Contextual Awareness during Selection of Moving Targets in Animated Stream Visualizations