Information about the world is often distributed among multiple sources, such as different experts or sensors in a sensor network. We study large-scale open systems for aggregating such information. In the Opensense project, we consider aggregating information from mobile and fixed air quality sensors into a single coherent air quality map. For sentiment analysis, we develop games that extract human judgement on the sentiment features in texts. This provides data for active learning algorithms as well as benchmarks for evaluating learning results. We thus hope to replace the massive datasets that are commonly used in machine learning approaches.
Another important issue is to provide incentives to provide high-quality, truthful information. We have developed game-theoretic schemes for sensor networks and for human computation platforms such as Amazon Mechanical Turk.