Leo

About

Distributed and heterogeneous data and knowledge

About

Team presentation

Research themes

International and industrial relations

News

Events

Team presentation

Research themes

International and industrial relations

News

Events

DB Seminar by Konstantinos Karanasos

DB Seminar by Ioana Manolescu

Xiao Bai: Toward Distributed Search

Jorge Quiane: Managing Very Large Datasets in a Cloudy World

Kostas Stefanidis: Contextual Database Preferences

DB Seminar by Zoi Kaoudi

Blogroll

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Copyright

International:

National:

International:

National:

The current explosion in the volume and complexity of data produced and consumed by individuals or organization is unparalleled. The development of the Internet, of data and knowledge interchange standards such as XML, RDF, Web Services, OWL, and the recent democratization of access to large-scale distributed computer clouds opens unprecedented opportunities for large-scale data management and reasoning.

Leo brings together INRIA personnel with researchers from the LRI‘s Artifficial Ingelligence (IASI) and Databases (DB) groups. Our work aims at exploiting these infrastructures to devise and deploy innovative, efficient, distributed data- and knowledge-rich platforms.

You can browse the 2011 activity report and the presentation slides from the INRIA evaluation, October 2011.

The broad areas of our research are:

modeling and organizing data, reasoning, and knowledge through formal techniques;
exploiting and enriching data semantics to enhance its value and reach full interoperability;
designing algorithms and deploying systems that efficiently tackle the complex and growing data management needs.

Leo researchers work closely with colleagues from UCSD (USA), Politecnico di Milano (Italy), U. Basilicata and U. Pisa (Italy), NICTA (Australia), TU Berlin (Germany), TU Delft (The Netherland).

We work in close collaborations with other INRIA teams (ATLAS, AVIZ, EXMO, MOSTRARE), LRI teams (FORTESSE), and higher education institutions such as Supelec, UVSQ, Univ. Paris 1 (La Sorbonne).
Our industrial partners include Thales, EADS, Bongrain, Renault Trucks, Mandriva.

Permanent link to this article: http://team.inria.fr/leo/

11 DB Seminar by Konstantinos Karanasos 15:00 – 17:00 LRI, Bâtiment 650 (PCRI), salle 455 DB Seminar by Konstantinos Karanasos iCal "XQuery view-based query rewriting"

18 DB Seminar by Ioana Manolescu 15:00 – 17:00 LRI, Bâtiment 650 (PCRI), salle 455 DB Seminar by Ioana Manolescu iCal "An Algebra for XML Queries"

20 Xiao Bai: Toward Distributed Search 14:00 – 15:00 LRI, Bâtiment 650 (PCRI), salle 455 Xiao Bai: Toward Distributed Search iCal Abstract: The rapid increasing amount of data on the Web provides a huge source of information but makes efficient search more challenging. Distributing the search is appealing to improve both efficiency and scalability. In this talk, we first present, in the context of social tagging systems, two gossip-based approaches that personalize query processing in a peer-to-peer manner. The off-line approach relies on user's past behavior to personalize the search, and the on-line approach relies on user's past behavior and current query to further improve the result quality for queries depicting user's emerging interests. We then present, for a multi-site search engine, two approaches that invalidate entries in result cache to guarantee the freshness of results served to users. The on-line invalidation approach invalidates an entry upon cache hit according to index changes in local search site. The threshold-based approach makes invalidations when index changes in remote search sites. Joint use of both approaches provides a promising solution to react to index updating that may arise in future multi-site search engines.

25 Jorge Quiane: Managing Very Large Datasets in a Cloudy World 15:45 – 16:45 LRI, Bâtiment 650 (PCRI), salle 445 Jorge Quiane: Managing Very Large Datasets in a Cloudy World iCal Abstract: Nowadays, many enterprises and organizations are faced with large volumes of data that have to be analyzed in a per-day basis. In particular, scientific datasets are growing at unprecedented rates and are likely to continue growing to the order of Exabytes. These current needs of data management require applications to run over a large number of computing nodes. However, databases management systems (DBMS) have proven inefficient to deal with very large datasets as well as to scale out to a large number of computing nodes. In this context, MapReduce and the Cloud computing are two alternative technologies that respond to this challenge. While MapReduce allows enterprises, organizations, and researchers to easily process very large volumes of data, the Cloud provides the required computing infrastructure to scale applications out to a large number of computing nodes. The beauty of these approaches are their ease-to-use and almost-free-admin cost properties. However, this simplicity comes at a price: the performance of MapReduce applications in the Cloud often do not match the one of a well-configured parallel DBMS. In this talk, we present some of the main features that allow DBMS to achieve orders of magnitude better performance than MapReduce applications. Then, we analyze how our Hadoop++ project allows MapReduce applications to match DBMS performance in the Cloud. We also discussed the design choices we made in the Hadoop++ project in order to preserve the ease-of-use and the almost-free-admin cost of MapReduce applications in the Cloud. Finally, we conclude this talk by discussing some of the challenges imposed by the Cloud to achieve data management efficiently.

27 Kostas Stefanidis: Contextual Database Preferences 14:00 – 15:30 LRI, Bâtiment 650 (PCRI), salle 445 Kostas Stefanidis: Contextual Database Preferences iCal Abstract As both the volume of data and the diversity of users accessing them increase, user preferences offer a useful means towards improving the relevance of the query results to the information needs of the specific user posing the query. In this talk, we will focus on enhancing preferences with context. Context may express conditions on situations external to the database or related to the data stored in the database. We will outline models for expressing both types of preferences. Then, given a user query and its surrounding context, we will consider the problem of selecting related preferences to personalize the query.