Scolopax: Exploratory Analysis of Scientific Data

The formulation of hypotheses based on patterns found in data is an essential component of scientific discovery. As larger and richer data sets become available, new scalable and user-friendly tools for scientific discovery through data analysis are needed. We demonstrate Scolopax, which explores the idea of a search engine for hypotheses. It has an intuitive user interface that supports sophisticated queries. Scolopax can explore a huge space of possible hypotheses, returning a ranked list of those that best match the user preferences. To scale to large and complex data sets, Scolopax relies on parallel data management and mining techniques. These include model training, efficient model summary generation, and novel parallel join techniques that together with traditional approaches such as clustering manipulate massive model-summary collections to find the most interesting hypotheses. This demonstration of Scolopax uses a real observational data set, provided by the Cornell Lab of Ornithology. It contains more than 3.3 million bird sightings reported by citizen scientists and has almost 2500 attributes. Conference attendees have the opportunity to make novel discoveries in this data set, ranging from identifying variables that strongly affect bird populations in specific regions to detecting more sophisticated patterns such as habitat competition and migration.