David Reymond

Automatic websites classification and retrieval using websites communication signatures

Automated classification and summarization of websites, as well as knowledge retrieval from web contents, are central challenges for performing accurate and focused webometrics studies. As global approaches based on open web and full webpages content fail to cope with such challenges, in this paper we first focus our approach on organizational and institutional websites, and secondly, we consider the communicational value of the data provided, especially on navigation menus, as a central information source. Another key point of our approach is that we more especially focus on the exploitation of a recent unsupervised classification technique, that not only automatically groups together websites sharing a number of features but also explicitly associates each website class with a set of specific features, or labels, characteristic of that class. As compared to a supervised classification, our approach presents the main advantage to cope with the scaling problem whilst, as we show in our experiment, providing superior performance through efficient characterization of latent unlabeled websites classes.

Data-mining and indicators in cyberinfrastructure

Aside the e-learning tools, softwares, platforms or frameworks, educational organizations provide to their students and staff a variety of digital services through their web portals or cyber infrastructure. Measuring its global usage would be firstly a benefit for policy makers to make decision on objective data, but would provide also useful information to guide on web services tools customization, maintenance or development strategy. We present a non-intrusive, open-source and extensible technical solution that can provide usage indicators on a majority of web digital service environment. The tool can be settled up recursively up in order to provide local, regional or national usage indicators. We will show how the system opens the way to targeted behavioral studies in the context of a performative quality approach of digital services provided by educational organizations and can be mapped to e-learning performance analysis. The examples of indicators creation will be taken from real situations, that is to say in the context of higher education in France.