by Mark Davis
Semantic zooming involves providing the right type of information depending on the resolution of viewer. A canonical example is the map viewer, where country outlines are visible at one level and, as the user zooms in, provinces and roadways become increasingly visible. High-performance zooming technologies are critically dependent on the efficient materialization of views from the data resources and, for big data resources like sensor data, econometrics, social networks, biological databases, and networking performance data, they are impeded by the scale of the data and the need to preprocess the information into aggregate views in advance, reducing the granularity and timeliness of the insights that can be obtained from the zooming technology. Through parallelization, however, semantic zooming that operates directly on the data becomes possible. In this highly visual presentation and demo, we will show our ZettaZoom visualization engine that provides a protocol for Hadoop and HBase marshaling of data signals into visual representations that preserve the relationships present within the data, enabling semantic zooming over massive data collections.
13th–14th June 2012