Description: The scientific inquiry process for simulation and observational data starts with generating or collecting voluminous data quantities that then require analysis to find physics features of interest and ultimately support drawing conclusions about the processes. One common way scientists would like to use to identify data features is to use a query on the derived quantities from the raw data. However, computing everything and processing raw data is prohibitively expensive. In this project we explore the design of a new, robust, scalable metadata management system, along with the learning models using various potential metrics to support data placement, management, and query support to offer an improved time-to-insight — an important metric of scientific output. The project includes both research activities and software development.
The Scalable Computing Software Lab (SCS Lab) at Illinois Tech is a research lab focusing on High-Performance Computing (HPC) technologies with an expertise in scalable memory and storage systems. The SCS Lab has more than 20 years history with tremendous impact in the scientific community. The impact of the SCS lab is tremendous with hundreds of technical papers, thousands of citations, dozens of patented technologies and software tools, hundreds of alumni, and more than 20 current members conducting innovative research and development today. Find out more at http://www.cs.iit.edu/~scs/
Interested or have questions? Contact: scslab@iit.edu or akougkas@iit.edu
