The WorldWide Telescope computer program, released to researchers and the public as a free resource in 2008 by Microsoft Research, has changed the way the ever-growing Universe of online astronomical data is viewed and understood. The WWT program can be thought of as a scriptable, interactive, richly visual browser of the multi-wavelength Sky as we see it from Earth, and of the Universe as we would travel within it. In its web API format, WWT is being used as a service to display professional research data. In its desktop format, WWT works in concert (thanks to SAMP and other IVOA standards) with more traditional research applications such as ds9, Aladin and TOPCAT. The WWT Ambassadors Program (founded in 2009) recruits and trains astrophysically-literate volunteers (including retirees) who use WWT as a teaching tool in online, classroom, and informal educational settings. Early quantitative studies of WWTA indicate that student experiences with WWT enhance science learning dramatically. Thanks to the wealth of data it can access, and the growing number of services to which it connects, WWT is now a key linking technology in the Seamless Astronomy environment we seek to oer researchers, teachers, and students alike.
The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.
Throughout history, astronomers have been accustomed to data falling from the sky. But our relatively newfound ability to store the sky's data in "clouds" offers us fascinating new ways to access, distribute, use, and analyze data, both in research and in education. Here we consider three interrelated questions: (1) What trends have we seen, and will soon see, in the growth of image and data collection from telescopes? (2) How might we address the growing challenge of finding the proverbial needle in the haystack of this data to facilitate scientific discovery? (3) What visualization and analytic opportunities does the future hold?