Alyssa Pena

2papers

2 Papers

HCJan 16, 2018
ProvThreads: Analytic Provenance Visualization and Segmentation

Sina Mohseni, Alyssa Pena, Eric D. Ragan

Our work aims to generate visualizations to enable meta-analysis of analytic provenance and aid better understanding of analysts' strategies during exploratory text analysis. We introduce ProvThreads, a visual analytics approach that incorporates interactive topic modeling outcomes to illustrate relationships between user interactions and the data topics under investigation. ProvThreads uses a series of continuous analysis paths called topic threads to demonstrate both topic coverage and the progression of an investigation over time. As an analyst interacts with different pieces of data during the analysis, interactions are logged and used to track user interests in topics over time. A line chart shows different amounts of interest in multiple topics over the duration of the analysis. We discuss how different configurations of ProvThreads can be used to reveal changes in focus throughout an analysis.

HCJan 16, 2018
Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs

Sina Mohseni, Andrew Pachuilo, Ehsanul Haque Nirjhar et al.

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.