Interactive Exploration Framework for Big Data Sets

Big data issues are the order of the day for many researchers in astronomy. In the past, several machine learning methods were proposed to organize, classify, or condense big data sets. However, this is not the end of the road. In most cases, researchers need to take further analysis by hand on automatically preprocessed data to gather valuable conclusions. To facilitate the pipeline of data analysis, we suggest a generic front-end framework allowing the user not only to process the data automatically, but also to interactively explore and investigate the results of machine learning procedures. A compact visualization gives an initial overview and can be adjusted to point out the parts of interest. By providing abstract accommodation functions such as zooming, scrolling, filtering, and labeling, crucial data fragments can be found and marked in an intuitive way.