Jean-Marie Le Goff

Xavier Ouvrard, Jean-Marie Le Goff, Stéphane Marchand-Maillet

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.

Xavier Ouvrard, Jean-Marie Le Goff, Stephane Marchand-Maillet

Most networks tend to show complex and multiple relationships between entities. Networks are usually modeled by graphs or hypergraphs; nonetheless a given entity can occur many times in a relationship: this brings the need to deal with multisets instead of sets or simple edges. Diffusion processes are useful to highlight interesting parts of a network: they usually start with a stroke at one vertex and diffuse throughout the network to reach a uniform distribution. Several iterations of the process are required prior to reaching a stable solution. We propose an alternative solution to highlighting the main components of a network using a diffusion process based on exchanges: it is an iterative two-phase step exchange process. This process allows to evaluate the importance not only of the vertices but also of the regrouping level. To model the diffusion process, we extend the concept of hypergraphs that are families of sets to families of multisets, that we call hb-graphs. This version is an extended version of arXiv:1809.00190v1: the overlaps with the v1 are in black, the new content is in blue. The contributions of this extended version are: the proofs of conservation and convergence of the extracted sequences of the diffusion process, as well as the illustration of the speed of convergence and comparison to classical and modified random walks; the algorithms of the exchange-based diffusion and the modified random walk; the application to a use case based on Arxiv publications. All the figures except one have been either modified or added in this extended version to take into account the new developments.

Adam Agocs, Jean-Marie Le Goff

Data visualisation assists domain experts in understanding their data and helps them make critical decisions. Enhancing their cognitive insight essentially relies on the capability of combining domain-specific semantic information with concepts extracted out of the data and visualizing the resulting networks. Data scientists have the challenge of providing tools able to handle the overall network lifecycle. In this paper, we present how the combination of two powerful technologies namely the REST architecture style and JSON Schema/JSON Meta Schema enable data scientists to use a RESTful web service that permits the construction of knowledge graphs, one of the preferred representations of large and semantically rich networks.

Adam Agocs, Dimitrios Dardanis, Jean-Marie Le Goff et al.

Human reasoning in visual analytics of data networks relies mainly on the quality of visual perception and the capability of interactively exploring the data from different facets. Visual quality strongly depends on networks' size and dimensional complexity while network exploration capability on the intuitiveness and expressiveness of user frontends. The approach taken in this paper aims at addressing the above by decomposing data networks into multiple networks of smaller dimensions and building an interactive graph query language that supports full navigation across the sub-networks. Within sub-networks of reduced dimensionality, structural abstraction and semantic techniques can then be used to enhance visual perception further.

Andrew Branson, Richard McClatchey, Jean-Marie Le Goff et al.

Software engineers frequently face the challenge of developing systems whose requirements are likely to change in order to adapt to organizational reconfigurations or other external pressures. Evolving requirements present difficulties, especially in environments in which business agility demands shorter development times and responsive prototyping. This paper uses a study from CERN in Geneva to address these research questions by employing a description-driven approach that is responsive to changes in user requirements and that facilitates dynamic system reconfiguration. The study describes how handling descriptions of objects in practice alongside their instances (making the objects self-describing) can mediate the effects of evolving user requirements on system development. This paper reports on and draws lessons from the practical use of a description-driven system over time. It also identifies lessons that can be learned from adopting such a self-describing description-driven approach in future software development.