CALL FOR PAPERS
Two different lines of research have developed around the topic of making inferences from data. One line arises from the analysis of physical data captured directly by sensors of real-world behaviors. These data include streams of GPS and WiFi sensors, accelerometers, compasses, gyroscopes, audio, and video, which are often processed to infer location, motion, co-presence, and identity. Another line arises from data captured about online behaviors. These include email messages, web browsing histories, application traces, keystroke loggers, online calendars, document repositories, tagging databases, and social networks. Analysis of online data sources is performed to infer topics of interest, retrieve information, and enhance collaboration.
Will these two types of data streams remain separate? Or will they come together in the near future? Smartphones have made it easy to collect physical data about people. Web 2.0 technologies have encouraged users to create and curate more information online. Together, these techniques could make inferences more accurate, more detailed, more "24-7" than ever before, and make completely new kinds of inferences possible.
However, there are challenges. It is not clear how to combine the different data sources. Because of the differences in types, and large amount of data involved, it may not be obvious how to combine data and inferences together into a useful application. The workshop has the following goals:
before 11:59pm EST (American East Coast Time) on
Kurt Partridge, Palo Alto Research Center
Oliver Brdiczka, Palo Alto Research Center
Judy Kay, University of Sydney
Bob Kummerfeld, University of Sydney
Paul Lukowicz, Universität Passau
Kai Kunze, Universität Passau
Partially sponsored by the ALLOW project.