Web interface for "A Visual Analytics Framework for Reviewing Streaming Performance Data".

Visual analytics framework for exploring and analyzing performance data from the ROSS PDES engine.

See interface demo.

Requirements

Follow the installation instructions for the listed algorithms from mv-streaming-algorithms.

• Progressive causality
• Progressive K-means clustering
• Incremental PCA-based change point detection
• Progressive Incremental PCA

Note: Virtualenv is recommended for running the server backend:

virtualenv -p python3 venv
source venv/bin/activate

Note: Works and tested on python version >= 3.6

Server

App server with streaming data support for developing data analytics and visualization applications to analyze the performance of the ROSS simulator engine.

Installation

cd server
pip install -r requirements.txt

Running

The app server listens for HTTP and WebSocket requests on 8888 and receives data streams on port 8000:

 python server.py --http=8888 --datafile=./$DATA_SRC/$DATA_FILE.bin --appdir=../ross-vis/dist' 

App server

Installation

cd public
npm install

Running

npm run dev

After the server is started with HTTP port=8888, open the client app at http://localhost:8080.

Citation

Suraj P. Kesavan, Takanori Fujiwara, Jianping Kelvin Li, Caitlin Ross, Misbah Mubarak, Christopher D. Carothers, Robert B. Ross, and Kwan-Liu Ma. "A Visual Analytics Framework for Reviewing Streaming Performance Data." In Proceedings of IEEE Pacific Visualization Symposium (PacificVis), forthcoming