Visual and interactive analysis of a large collection of open data with the relative neighborhood graph

Abstract

We deal in this paper with the problem of creating an interactive and visual map for a large collection of Open datasets. We first describe how to define a representation space for such data. We use text mining techniques to create features. Then, with a similarity measure between Open datasets, we use the Relative Neighbors method for building a proximity graph between datasets. We use a force-directed layout method to visualize the graph (Tulip Software). We present the results with a collection of 300,000 datasets from the French Open data web site, in which the display of the graph is limited to 150,000 datasets. We study the discovered clusters and we show how they can be used to browse this large collection.