The system is deployed to: https://vdl.sci.utah.edu/predicting-intent
The purpose of this tool is to predict user intents in the form of patterns when brushing in scatterplots.
This project is developed at the Visualization Design Lab at the SCI Institute at the University of Utah by Kiran Gadhave, Jochen Görtler, Zach Cutler, and Alexander Lex, with contributions by Jeff Phillips, Miriah Meyer, and Oliver Deussen.
Please visit the publication page for more details and information on how to cite this work. The source code and documentation is available here. This project is funded by the National Science Foundation trough grant IIS 1751238 .
- Live study interface: http://vdl.sci.utah.edu/predicting-intent-study/
- The code to generate stimulus, tasks, code results and cool notebook to process the crowdsource results: https://github.com/visdesignlab/intent-data-generation/
- R analysis and original data: https://github.com/visdesignlab/intent-study-analysis/
- The provenance data exploration website: https://vdl.sci.utah.edu/intent-study-analysis/
- Source code of the provenance library: https://github.com/visdesignlab/provenance-lib-core/
To build the server you need python3, pipenv and yarn package manager.
After cloning the repository for the first time, ensure you have python3 and pipenv, then run:
yarn run build-envTo start the server run:
yarn start