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Selected R&D projects

Scientific person in charge

  • (2010-2013) DIGITEO - Levetove project (Funded by Ile de France): The project aims at developing novel techiques and methods for large scale grah and web mining
  • (2006-2008) IST/SQO-OSS(IST-2005-33331): Source Quality Observatory for Open Source Software - subcontractor. Data Mining methodologies for metrics in software repositories
  • (2003-2007) PENED/GSRT: Funding for five Ph.D. fellowships in the area of large scale distributed data mining.
  • (2001-2004) - IST/h-TechSight (IST-2001-33174) - subcontractor. Web Mining for Chemical Engineering Portals.
  • (2001-2004) - IST/NEMIS (IST-2001-37574) - subcontractor. Text Mining Excellence Network
  • (2001-2004) IST/FET/PANDA thematic network, Research on Pattern Based DBMSs
  • (2001-2003) IST/FET-OPEN/DBGlobe: A Data-centric Approach to Global Computing
  • (2001-2003) IST/i-KnowUMine, Web Site Restructuring based on usage knowledge
  • (1999-2001) PENED/GSRT: Research & Development for Knowledge Discovery in Medical Data (with Medical School, Univ. of Athens), Scientist in charge - see relevant prototype

DIGITEO Chair Grant

Professor Vazirgiannis currently held a DIGITEO Chair for the period 2010-2012 funded by the DIGITEO alliance. The objective of the project was to conduct state of the art research in the areas of data mining and machine learning for the Web 2.0.

Project Summary: Web 2.0 prevails in the last years as a platform that enables and harnesses novel features such as user contribution (reviews, comments), collective intelligence (PageRank, folksonomies, popularity), community and sense of ownership, the building of social networks, rich user interface, while it functions like a traditional application. We witness new forms of participation in the creation exchange and aggregation of content that present different requirements than traditional static Web content; furthermore, the Web is more and more used as a support for applications that have their own information logic, such as social networks, wikis, blogs, not to mention office cloud-based application (like Google Docs). Given the huge and continuous change pace, it is becoming increasingly harder to make a current state available to the users in terms of searching/aggregation mechanisms. On the other hand predictions in this context are becoming increasingly important from the economic point of view as well - for instance social network users or web pages that will become important are attractive targets for marketing actions.

The overall objective of the proposed project is mining and learning from the large scale and dynamically evolving data and graphs generated in the Web 2.0 context. We seek to understand the structure and dynamics of a Web 2.0 information collection (using the Web graph and social networks as use cases) and to learn how to predict future properties and behavior based on its past evolution. We focus on the users and the content of the evolving Web 2.0 collections taking into account temporal evolution towards valid rankings.

The expected benefits of the project are dual. On the one hand the methodological aspects including integration and extension of advanced learning methods in the challenging context of Web 2.0 data. On the other hand the concrete resulting innovative methods will be integrated in the function of the Web or Telecoms industry.

Supporting Organizations: