Retrieval and exploratory search in multivariate research data repositories using regressional features
Maximilian Scherer, Jürgen Bernard, Tobias Schreck · 2011
Increasing amounts of data are collected in many areas of research and application. The degree to which this data can be accessed, retrieved, and analyzed is decisive to obtain progress in fields such as scientific research or industrial production. We present a novel method supporting content-based retrieval and exploratory search in repositories of multivariate research data. In particular, functional dependencies are a key characteristic of data that researchers are often interested in. Our methods are able to describe the functional form of such dependencies, e.g., the relationship between inflation and unemployment in economics. Our basic idea is to use feature vectors based on the goodness-of-fit of a set of regression models, to describe the data mathematically. We denote this approach Regressional Features and use it for content-based search and, since our approach motivates an intuitive definition of interestingness, for exploring the most interesting data. We apply our method on considerable real-world research datasets, showing the usefulness of our approach for user-centered access to research data in a Digital Library system.