Statistics – Computation
Scientific paper
Jan 2010
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010aas...21547702g&link_type=abstract
American Astronomical Society, AAS Meeting #215, #477.02; Bulletin of the American Astronomical Society, Vol. 42, p.563
Statistics
Computation
Scientific paper
First-generation citizen science projects (such as Galaxy Zoo) have proven very successful at utilizing the efforts of volunteers (human computation) to attack specific problem areas, such as certain types of image analysis, where machines are not currently especially effective. However, there has been little or no attempt in these to capture or transfer the domain knowledge from carbon-based to silicon-based systems.
AstroCollation is a next generation collaborative science venture that aims to foster a more synergistic relationship between humans and machines around transient astronomy. Data mining algorithms will be applied to transient event data to produce conceptual models describing them. These models will then be presented to citizen scientists for value judgments, deciding which of a set of models provides the best description, as well as providing contextual information to aid the classification process. Finally decisions and information are factored back into the system and consolidated to produce a consensus description of an event.
The project is predicated upon semantic technologies - allowing the formal representation of conceptual relations within a particular domain - and will leverage data and infrastructure from the Catalina Real-time Transient Survey and SkyAlert system. It will address such issues as the formal treatment of uncertainty in data, how to map a multistage computational process to a simple and minimal interface or set of interfaces, and how to fold human computation into a workflow process where time criticality can be important.
Djorgovski Stanislav G.
Donalek Ciro
Drake Andrew J.
Graham Matthew
Mahabal Ashish A.
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