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Serge Abiteboul
Researcher at French Institute for Research in Computer Science and Automation
Publications - 279
Citations - 25002
Serge Abiteboul is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: XML & Query language. The author has an hindex of 73, co-authored 278 publications receiving 24576 citations. Previous affiliations of Serge Abiteboul include University of Southern California & PSL Research University.
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Proceedings ArticleDOI
On the representation and querying of sets of possible worlds
TL;DR: The approach is algebraic but the authors' bounds also apply to logical databases, and it is shown that the approach is, in a sense, the best possible, by deriving two NP-completeness lower bounds for the bounded possible fact problem when the fixed query contains either negation or recursion.
Posted Content
PARIS: Probabilistic Alignment of Relations, Instances, and Schema
TL;DR: Paris as mentioned in this paper is a probabilistic approach for ontology alignment, i.e., it measures degrees of matchings based on probability estimates, and it can align not only instances, but also relations and classes.
Journal ArticleDOI
Datalog extensions for database queries and updates
Serge Abiteboul,Victor Vianu +1 more
TL;DR: Deterministic and non-deterministic extensions of Datalog with fixpoint semantics are proposed, and their expressive power characterized, to overcome the limited expressive power available with purely declarative semantics.
Journal ArticleDOI
Querying Documents in Object Databases
TL;DR: It is shown that almost standard database optimization techniques can be used to answer queries without having to load the entire document into the database, and the interaction of full-text indexes with standard database collection indexes that provide important speed-up are considered.
Proceedings ArticleDOI
Extracting schema from semistructured data
TL;DR: It is established that the general problem of finding an optimal form of semistructured data based on labeled, directed graphs is NP-hard, but some heuristics and techniques based on clustering that allow efficient and near-optimal treatment of the problem are presented.