Visual-Interactive Querying for Multivariate Research Data Repositories Using Bag-of-Words

Abstract

Large amounts of multivariate data are collected in different areas of scientific research and industrial production. These data are collected, archived and made publicly available by research data repositories. In addition to meta-data based access, content-based approaches are highly desirable to effectively retrieve, discover and analyze data sets of interest. Several such methods, that allow users to search for particular curve progressions, have been proposed. However, a major challenge when providing content-based access -- interactive feedback during query formulation -- has not received much attention yet. This is important because it can substantially improve the user's search effectiveness. In this paper, we present a novel interactive feedback approach for content-based access to multivariate research data. Thereby, we enable query modalities that were not available for multivariate data before. We provide instant search results and highlight query patterns in the result set. Real-time search suggestions give an overview of important patterns to look for in the data repository. For this purpose, we develop a bag-of-words index for multivariate data as the back-end of our approach. We apply our method to a large repository of multivariate data from the climate research domain. We describe a use-case for the discovery of interesting patterns in maritime climate research using our new visual-interactive query tools.