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Microsoft offers scientists data analytics tool

The software giant gives away Daytona, a tool that helps scientists analyze large data sets so they can focus on doing research and not coding.

Jay Greene Former Staff Writer
Jay Greene, a CNET senior writer, works from Seattle and focuses on investigations and analysis. He's a former Seattle bureau chief for BusinessWeek and author of the book "Design Is How It Works: How the Smartest Companies Turn Products into Icons" (Penguin/Portfolio).
Jay Greene

Microsoft unveiled new technology today designed to give academics better tools to harness the vast quantities of data available to them.

"We're living in a data deluge right now," said Tony Hey, corporate vice president of Microsoft Research Connections.

Scientists generate massive data in their work in areas such as environmental science, particle physics, astronomy, and other disciplines. Analyzing that information becomes ever more cumbersome.

So Microsoft released Daytona, a tool kit that lets scientists run a wide variety of analytics and machine-learning algorithms on Windows Azure. The technology is intended to free up those scientists from having to code their own software tools, giving them the ability to analyze their largest data collections and focus on their work.

"It allows scientists to do science without having to build software," Hey said.

The genesis of Daytona came after a meeting hosted by the Office of Cyberinfrastructure at the National Science Foundation. Roger Barga, an architect in the eXtreme Computing Group in Microsoft Research, noted that the attendees asked for computational runtime for data analysis and machine learning over their data sets. That led Microsoft to create the tool, which it's making available freely to that community.

The announcement comes during Microsoft's 12th annual Faculty Research Summit, a gathering of computer scientists, academics, educators, and government officials at the company's Redmond, Wash., headquarters. This year's event, with 300 attendees, is focused on research around natural user interface technology, cloud computing, and machine learning.