ANR

PIMI

Personal Information Management through Internet

CBOD

Cloud Based Organizational Design

ABLISS

Automating Building from Literature of Signalling Systems

ALICIA

Adaptative learning for intelligent crowdsourcing and information access

WebSTAND-Gemo

The objective of this ANR was to analyze the problems surrounding the use of semi-structured databases in social sciences. This ANR regrouped both computer science and sociol- ogy laboratories. Work done in Gemo included XML data cleaning and work on automatic selection and maintenance of materialized XML views. I. Manolescu was the scientific director for the IASI/Gemo team.

WEBSTAND BD

The objective of this ANR (2006-09) is to analyze the problems surrounding the use of semi-structured databases in social sciences. This ANR regroups both computer science and sociology laboratories. Work done in Gemo which contributes to WebStand includes XML data cleaning and work on automatic selection and maintenance of materialized XML views.

WebContent

The WebContent project is creating a software platform to accommodate the tools necessary to ef- ficiently exploit and extend the future of the Internet : the Semantic Web. The first targeted domain is the watch, a subpart of intelligence dedicated to warn the decider on the occurrence of an event or the evolution of a situation.

UNLOC

In UNLOC, we focused on incomplete search for UNSAT, a challenging and promising question, and developed new learning schemes for Conflict Directed Clause Learning algorithms. L. Simon was the coordinator of this project.

QUALINCA

Qualinca is a ANR Contint funded research project looking at developing mechanisms allowing to quantify the quality level of a bibliographical knowledge base, to improve the afore mentioned quality level, to maintain the quality when updating the knowledge base and to exploit the knowledge bases taking into account their quality levels.

PAGODA

PAGODA (Practical algorithms for ontology-based data access) is a basic research project whose ob- jective is to improve the efficiency and robustness of ontology-based data access by developing scalable algorithms for query answering in the presence of ontologies as well as pragmatic approaches to handling inconsistent data.

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