The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets
This addresses the need for better browsing tools for researchers or users exploring complex multimedia datasets, though it appears incremental as it builds on existing concepts like facets and co-occurrence networks.
The paper tackles the problem of linear information retrieval in multimedia datasets by introducing a facet-based browsing system using HyperBag-Graphs, enabling simultaneous visualization of multiple facets like keywords and authors to enhance exploration and insights.
Traditional verbatim browsers give back information in a linear way according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s$\cdot$he wants to explore other facets of a multi-facetted information space. For instance, in a multimedia dataset different facets such as keywords, authors, publication category, organisations and figures can be of interest. The facet simultaneous visualisation can help to gain insights on the information retrieved and call for further searches. Facets are co-occurence networks, modeled by HyperBag-Graphs -- families of multisets -- and are in fact linked not only to the publication itself, but to any chosen reference. These references allow to navigate inside the dataset and perform visual queries. We explore here the case of scientific publications based on Arxiv searches.