1. Digital Science Center III
Geoffrey C. Fox, David Crandall, Judy Qiu, Gregor von Laszewski, Fugang Wang, Badi' Abdul-Wahid,
Saliya Ekanayake, Supun Kamburugamuva, Jerome Mitchell, Bingjing Zhang, Pulasthi Wickramasinghe, Hyungro Lee, Andrew Younge
School of Informatics and Computing, Indiana University
Scientific Impact Metrics
WebPlotViz – Browser Visualization of High Dimensional Data
Visualization of Stock market as a high dimensional time series
We developed a software framework and process to evaluate scientific impact for XSEDE. We calculate and track various Scientific Impact Metrics of XSEDE., BlueWaters, and NCAR. Recently we conducted an updated peers comparison analysis with newly added and updated data, which shows consistent results as the previous one. During this process we retrieved and processed millions of data entries from multiple sources in various formats to obtain the result.
Summary of Streaming Workshops
NSF DoE and AFOSR funding October Indianapolis STREAM2015
March 22-23, 2016 Washington DC, STREAM2016
WebPlotViz is a 2D/3D data point browser that can visualize very large volumes of 2D or 3D data, as points in a virtual space and enable users to explore the virtual space interactively. WebPlotViz also includes support for Time Series Data plots
In this project we visualize stocks by projecting the correlations between stocks through time in to 3D using MDS. Years of historical daily stock data is segmented using a sliding window approach to create a continuous visualizations through time.
Data: Obtained daily stock values using The Center for Research in Security Prices (CSRP)1 database through the Wharton Research Data Services (WRDS) web interface
Experiments available:
Figures tracking various impact metrics for XSEDE (#pubs; #citations; H-Index; G-Index; etc.)
# Publications
Rank - Average
Rank - Median
# Citation - Average
# Citation - Median
XSEDE
5,081 59 63 28 12
Peers
356k 49 48 15 5
Table comparing XSEDE publication citation metrics with peers
Trajectories of stocks through time
Stock visualization of one time frame
446K sequences and ~100 clusters visualized in WebPlotViz
Main Components of SPIDAL Project
HPC-ABDS Apache Big Data Stack
Data analytics for IoT devices in Cloud
We developed a framework to bring data from IoT devices to a cloud environment for real time data analysis. The framework consists of; Data collection nodes near the devices, Publish-subscribe brokers to bring data to cloud and Apache Storm coupled with other batch processing engines for data processing in cloud. Our data pipe line is Robot → Gateway → Message Brokers → Apache Storm.
Simultaneous Localization and Mapping(SLAM) is an example application built on top of our framework, where we exploit parallel data processing to speedup the expensive SLAM computation.