|Streaming Data Characterization (SDC)|
Co Investigator: Deborah L. McGuinness
Description: This project aims to leverage the novel notion of semantic importance to characterize the importance among the boundless streaming data, so as to provide better query results in terms of accuracy or recall, as well as improve the system response time.
|Streaming Hypothesis Reasoning (Shyre)|
Principal Investigator: William Smith and Deborah L. McGuinness
Description: AIM will advance streaming reasoning techniques to overcome a limitation in contemporary inference that performs analysis only over data in a fixed cache or a moving window. This research will lead to methods that continuously shed light on proposed hypotheses as new knowledge arrives from streams of propositions, with a particular emphasis on the effect that removing the expectation of completeness has on the soundness and performance of symbolic deduction platforms.
Lead Professor: Peter Fox
Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.
At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.
At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.
Lead Professor: Jim Hendler, Deborah L. McGuinness