Scalable Analysis; Group Expertise
Take Advantage of the SAGE Consortium
Who is SAGE?
SAGE is a partnership among three entities – Sandia National Laboratories, the University of Utah's SCI Institute and Kitware, Inc. – committed to advancing technology and research to solve large data analysis problems. The SAGE consortium was formed in order to:
- Impact scalable analytics research at the national level, promoting policy and direction through innovative leadership, positive collaboration and advanced technology.
- Impact the needs of federal research agencies, academic institutions, and commercial partners through development, promotion and dissemination of open source and collaborative technologies.
What SAGE brings to the table is a combined force of passion and expertise spanning the entire pipeline of large scale data processing. SAGE consortium members are renowned for their research in advanced visualization technologies, scalable data and image analysis, scientific data management, novel algorithms, and data provenance. These strengths, combined with a successful background in scientific software engineering and development, provide a unique platform for collaboration and the development of custom commercial applications for your research needs.
• Uncertainty visualization
• Large-scale visualization
• Large-scale data analysis and management
• Topological Methods
• Software Process
• Scalable databases
• Computer Vision
• HPC Software
• Video analysis
• Titan Toolkit
• CMake/CTest/CDash quality software process
• ViSUS large data streaming
• MIDAS platform for large-data hosting and interactive scientific publications
• Text analysis (publication databases, blogs, news, etc.)
• Cyber security
• Connectome (neural graphs)
• Biomedical database analysis
• Social networks
• Wide-area Video
• EOR (Ensembles of runs) analysis. A typical simulation consists of hundreds - perhaps thousands - of runs, each of which has slightly different input parameters, and produces different results. The analysis of the collective information from all of these runs presents specific, unique challenges.
• Analysis of complex features within scientific simulations/data - the analysis of which requires abstract visualization techniques more traditionally associated with information visualization.
Dr. David Rogers
Data Analysis and Visualization