Susan B. Davidson received the B.A. degree in Mathematics from Cornell University in 1978, and the M.A. and Ph.D. degrees in Electrical Engineering and Computer Science from Princeton University. Dr. Davidson joined the University of Pennsylvania in 1982, and is now the Weiss Professor of Computer and Information Science (CIS). She is an ACM Fellow, a Fulbright scholar, and formerly served as Department Chair of CIS and Deputy Dean of the School of Engineering and Applied Science. She was also a founding co-Director of the Center for Bioinformatics at UPenn (PCBI). The PCBI is a multi-school center spanning Medicine, Engineering and Applied Sciences, and Arts and Sciences, and is known for its pioneering work in database integration, genomic schema development, visualization tools, and annotation systems.

Dr. Davidson’s research interests include database and crowdsourced systems, bioinformatics, and scientific workflow systems. Within bioinformatics she is best known for her work in data integration strategies, with XML as a data exchange and integration strategy, and with provenance for scientific workflows.

by Francois Charoy
(LORIA)

Le crowdsourcing contextuel, en particulier le crowdsourcing spatial est de plus en plus utilisé pour produire, annoter, mettre à jour ou faire des recommandations sur des bases géographiques. Nous proposons d’utiliser cette approche pour conduire des campagnes de mesures et d’évaluations sur des villes en utilisant l’intelligence de la foule. Dans cette présentation, nous présentons l’état actuel de ce travail. Nous cherchons pour l’instant à valider par simulation différentes approches (patterns) permettant de faire collecter et analyser des données par des citoyens. Cette simulation pose plusieurs problèmes que nous présenterons.
(details here)

This talk is canceled and reported to undefined date.

by Irini Fundulaki
(Institute of Computer Science, Greece)

In this talk we are going to present the Semantic Publishing Instance Matching Benchmark (SPIMBENCH) developed in the context of the Linked Data Benchmark Council EU project. SPIMBench allows the benchmarking of instance matching systems against not only structure-based and value-based test cases, but also against semantics-aware test cases based on OWL axioms. The benchmark features a scalable data generator and a weighted gold standard that can be used for debugging instance matching systems and for reporting how well they perform in various matching tasks.