ProvThreads: Analytic Provenance Visualization and Segmentation
January 16, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sina Mohseni, Alyssa Pena, Eric D. Ragan
arXiv ID
1801.05469
Category
cs.HC: Human-Computer Interaction
Citations
6
Venue
arXiv.org
Last Checked
4 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted