ORANGE, Calif. Chapman University is hosting Interface 2013 A Symposium on Big Data and Analytics. The main themes of the symposium are data science theory and practice, earth systems science data and health care data systems.
Topics, such as data from the astronomical sciences, data science and climate, large earth science data sets, the future of digital wallets, big data technology and analytics, health care data systems, benchmarking healthcare provider performance and more will be discussed. Scientists from Caltech, Stanford, George Mason and Vanderbilt Universities among others, as well as from NASA GSFC and JPL will be on hand to present. The event is closed to the public
"Big data is something we hear more and more about; this symposium will explore big data in the realms of theory, earth science and health care systems," said Hesham El-Askary, Ph.D., associate professor, director, hazards, global and environmental change and computational science programs, Schmid College of Science and Technology. "It should be noted that the scale of what is considered Big Data has been increasing steadily. Kilobytes (103), megabytes (106), gigabytes (109), and terabytes (1012) by now are familiar to any researcher using modern computer resources. The Earth Observing System of NASA introduced serious consideration of petabytes (1015). Data collection systems looming on the horizon such as the Large Synoptic Survey Telescope promise data on the scale of exabytes (1018). It is conceivable that data collection methods in the future may generate data sets of the scale of zettabytes (1021) and yottabytes (1024). The issue with big data is that computing power doubles every 18 months (Moore's Law), I/O bandwidth increases about 10% every year, but the amount of data doubles every year," El-Askary continued.
Major sponsors of Symposium include: Chapman University, Schmid College of Science and Technology, Center of Excellence in Earth Systems Modeling & Observations, Salford Systems, OCTANe OC, NEXUS IS, American Statistical Association Section on Statistical Computing, American Statistical Association Section on Statistical Learning and Data Mining, American Statistical Association Section on Statistical Graphics and National Institute of Statistical Science.
|Contact: Sheri Ledbetter|