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Managing Uncertainty in Networks with Declarative Overlays
We are entering an Industrial Revolution in the production of information. Whi le in the past data was "handmade" by typing on keyboards, today data is increas ingly manufactured by machines: sensors, cameras, software logs, etc. When harn essed in a timely manner, this data can have significant positive impact in many contexts, including early warning and rapid response in natural disasters, air quality monitoring, and improved Internet security. To provide useful informatio n in these contexts, computers in multiple locations must coordinate over networ ks, because the data is both widely distributed and massive, and cannot be "ware housed" at a single location in a timely manner. Worse, sensor data is typical "noisy" or erroneous in various ways, so statistical methods must be employed to convert the raw "evidence" data into probabilistically reliable information.
In this project we are developing new techniques to integrate statistical infere nce methods from AI with overlay network algorithms developed for settings inclu ding peer-to-peer, wireless, multicore and cloud computing.