Section: Application Domains
AVIZ develops active collaboration with users from various application domains, making sure it can support their specific needs. By studying similar problems in different domains, we can begin to generalize our results and have confidence that our solutions will work for a variety of applications. Our current application domains include:
Business Intelligence, in cooperation with EDF.
Social Network Analysis, in cooperation with France Telecom R&D, Univ. LIAFA, GET/ENST, and the French National Archives;
Biological research, in cooperation with INRA, the IGM Biological Research Laboratory at Univ. Paris-Sud and Institut Pasteur;
Digital Libraries, in cooperation with the French National Archives, the Bibilothèque Nationale and the Wikipedia community;
Global Security, in cooperation with training centers for urban communities at risk;
Business Intelligence aims at collecting and processing heterogeneous information to orient business decisions in term of product design or commercial offers. Both the quantity of information and the diversity of sites and formats where it can be collected is growing (e.g. Blogs and Social Network websites). We want to address the challenge of offering tools and components to quickly build analysis applications suited to these diverse inputs and the many specific tasks marketing analysts may attempt to do, helping them to quickly carry-out their work and produce understandable synthetic reports. We are working on such applications for EDF (see section 7.4 ).
Social Network Analysis
In the social networks domain, we are starting to work on exploratory visualization. Current studies in social networks presuppose that users know the nature of the networks they want to explore and the kinds of of transformations and layouts that will best suit their needs. This is often not true, and tools are very weak at helping users understand the nature of their networks and the transformations they could perform to get meaningful insights. This work began in 2004 with the arrival of Nathalie Henry in the Project. She is co-advised by Jean-Daniel Fekete and Peter Eades from the University of Sydney and NICTA, Australia.
We have been focusing on the use of the matrix representation to explore large graphs, building on our previous work using matrices for constraint-based programming. Matrices present challenging problems both interactively and mathematically. We are designing an interactive system to help users navigate and interact with large matrices. We are also preparing a survey on methods to reorder matrices, whether from graphs from tabular data.
Bioinformatics uses many complex data structures such as phylogenetic trees, genomes made of multi-scale parts (sequences of base pairs, genes, interaction pathways etc.) Biologists navigate through multitudes of these varied and complex structures daily in complex, changeable, data- and insight-driven paths. They also often need to edit these structures to annotate genes and add information about their functions. Visual Analytics is a powerful tool to help them, as we are currently pursuing in the Microbiogenomics project (see section 7.6 ).
In the digital Library domain, we collaborate with Wikipedia contributors to improve Wikipedia, as well as with historians such as the French National Archives on the National Center of Renaissance on exploratory projects to visualize and analyze historical documents (see 7.1 .)
In the global security domain, we collaborate with MASA Group (Mathématique Appliquée S.A.) and civil crisis managers from the CODAH (Communauté d'Agglomération Havraise) on interactive visualization tools for improving the quality of crisis management training exercices (see 7.2 .)
Part of our research consists in supporting traditional sciences with high-level tools to help analyze and make sense of large datasets. We apply our tools and techniques to biology, social sciences and to Wikipedia which has become a major supporting tool for scientists (see 7.1 ). We also design software infrastructures to help scientist perform their analytical tasks with high-level tools instead of having to learn complex tools requiring computer science skills (see 7.7 ).