Tova Milo - Gestion des données sur les foules

Organisé par : 
L’équipe "Keynotes" du LIG
Intervenant : 
Tova Milo
Tova Milo


Tova Milo received her Ph.D. degree in Computer Science from the Hebrew University, Jerusalem, in 1992. After graduating she worked at the INRIA research institute in Paris and at University of Toronto and returned to Israel in 1995, joining the School of Computer Science at Tel Aviv university, whereshe is now a full Professor. She is the head of the Database research group and holds the Chair of Information Management. She served as the Head of the Computer Science Department from 2011-2014. Her research focuses on advanced database applications such as data integration, XML and semi-structured information, Data centered Business Processes andCrowd-sourcing, studying both theoretical and practical aspects.

Tova served as the Program Chair of several international conferences, including PODS, VLDB, ICDT, XSym, and WebDB, and as a member of the VLDB Endowment and the ICDT executive board. She serves as the chair of the PODS Executive Committee and an editor of TODS and the Logical Methods in Computer Science Journal.

Tova has received grants from the Israel Science Foundation, the US-Israel Binational Science Foundation, the Israeli and French Ministry of Science and the European Union. She is an ACM Fellow, a member of Academia Europaea, and a recipient of the 2010 ACM PODS Alberto O. Mendelzon Test-of-Time Award and of the prestigious EU ERC Advanced Investigators grant.
"Réalisation technique : Antoine Orlandi | Tous droits réservés"

Modern data analysis combines general knowledge stored in databases with individual knowledge obtained from the crowd, capturing people habits and preferences. To account for such mixed knowledge, along with user interaction and optimization issues, data management platforms must employ a complex process of reasoning, automatic crowd-task generation and result analysis.

In this talk, I will introduce the notion of crowd mining and describe a generic architecture for crowd mining applications. This architecture allows us to examine and compare the components of existing crowdsourcing systems and point out extensions required by crowd mining. It also highlights new research challenges and potential reuse of existing techniques/components. I will exemplify this for the OASSIS project, a system developed in Tel Aviv University, and for other prominent crowdsourcing frameworks.