In the beginning was the news. And it was good. And the news multiplied… and multiplied… and multiplied. Drowning itself out in riotous cacophony.
So researchers at the University of Maryland developed NewsStand to help readers cut through the noise and find news that matters to them. It collects at least 50,000 news items from over 10,000 RSS feeds every day; automatically tags them by location and other attributes; and presents them on a map-based interface.
Join us as we talk with Hanan Samet, the 2011 ACM Paris Kanellakis Theory and Practice Award winner, as he tells us about Reading News with Maps.

Hanan Samet (http://www.cs.umd.edu/~hjs/) is a Distinguished University Professor of Computer Science at the University of Maryland, College Park and is a member of the Institute for Computer Studies.  He is also a member of the Computer Vision Laboratory at the Center for Automation Research where he leads a number of research projects on the use of hierarchical data structures for database applications, geographic information systems (GIS), computer graphics, computer vision, image processing, games, robotics, search, and textual representations of location as in the NewsStand (http://newsstand.umiacs.umd.edu) and TwitterStand (http://twitterstand.umiacs.umd.edu) which enable accessing a database of news articles and tweets using a map query interface.
For more information about the speaker visit: https://speakers.acm.org/speakers/samet_9203