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23 results on '"Leonid Libkin"'

3. The Linked Data Benchmark Council (LDBC): Driving Competition and Collaboration in the Graph Data Management Space.

Author

Gábor Szárnyas, Brad Bebee, Altan Birler, Alin Deutsch, George Fletcher 0001, Henry A. Gabb, Denise Gosnell, Alastair Green, Zhihui Guo, Keith W. Hare, Jan Hidders, Alexandru Iosup, Atanas Kiryakov, Tomas Kovatchev, Xinsheng Li, Leonid Libkin, Heng Lin, Xiaojian Luo, Arnau Prat-Pérez, David Püroja, Shipeng Qi, Oskar van Rest, Benjamin A. Steer, Dávid Szakállas, Bing Tong, Jack Waudby, Mingxi Wu, Bin Yang 0002, Wenyuan Yu, Chen Zhang, Jason Zhang, Yan Zhou, and Peter A. Boncz

Published
2023
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8. PG-Schema: Schemas for Property Graphs.

Author

Renzo Angles, Angela Bonifati, Stefania Dumbrava, George Fletcher 0001, Alastair Green, Jan Hidders, Bei Li, Leonid Libkin, Victor Marsault, Wim Martens, Filip Murlak, Stefan Plantikow, Ognjen Savkovic, Michael Schmidt, Juan Sequeda, Slawek Staworko, Dominik Tomaszuk, Hannes Voigt, Domagoj Vrgoc, Mingxi Wu, and Dusan Zivkovic

Published
2023
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9. Graph Pattern Matching in GQL and SQL/PGQ.

Author

Alin Deutsch, Nadime Francis, Alastair Green, Keith Hare, Bei Li, Leonid Libkin, Tobias Lindaaker, Victor Marsault, Wim Martens, Jan Michels, Filip Murlak, Stefan Plantikow, Petra Selmer, Oskar van Rest, Hannes Voigt, Domagoj Vrgoc, Mingxi Wu, and Fred Zemke

Published
2022
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16. PG-Schema: Schemas for Property Graphs.

Author

Renzo Angles, Angela Bonifati, Stefania Dumbrava, George Fletcher 0001, Alastair Green, Jan Hidders, Bei Li, Leonid Libkin, Victor Marsault, Wim Martens, Filip Murlak, Stefan Plantikow, Ognjen Savkovic, Michael Schmidt, Juan Sequeda, Slawek Staworko, Dominik Tomaszuk, Hannes Voigt, Domagoj Vrgoc, Mingxi Wu, and Dusan Zivkovic

Published
2022
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20. A Researcher’s Digest of GQL (Invited Talk)

Author

Nadime Francis and Amélie Gheerbrant and Paolo Guagliardo and Leonid Libkin and Victor Marsault and Wim Martens and Filip Murlak and Liat Peterfreund and Alexandra Rogova and Domagoj Vrgoč, Francis, Nadime, Gheerbrant, Amélie, Guagliardo, Paolo, Libkin, Leonid, Marsault, Victor, Martens, Wim, Murlak, Filip, Peterfreund, Liat, Rogova, Alexandra, Vrgoč, Domagoj, Nadime Francis and Amélie Gheerbrant and Paolo Guagliardo and Leonid Libkin and Victor Marsault and Wim Martens and Filip Murlak and Liat Peterfreund and Alexandra Rogova and Domagoj Vrgoč, Francis, Nadime, Gheerbrant, Amélie, Guagliardo, Paolo, Libkin, Leonid, Marsault, Victor, Martens, Wim, Murlak, Filip, Peterfreund, Liat, Rogova, Alexandra, and Vrgoč, Domagoj

Abstract

GQL (Graph Query Language) is being developed as a new ISO standard for graph query languages to play the same role for graph databases as SQL plays for relational. In parallel, an extension of SQL for querying property graphs, SQL/PGQ, is added to the SQL standard; it shares the graph pattern matching functionality with GQL. Both standards (not yet published) are hard-to-understand specifications of hundreds of pages. The goal of this paper is to present a digest of the language that is easy for the research community to understand, and thus to initiate research on these future standards for querying graphs. The paper concentrates on pattern matching features shared by GQL and SQL/PGQ, as well as querying facilities of GQL.

Published
2023
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21. Propositional and predicate logics of incomplete information

Author

Leonid Libkin, Marco Console, Paolo Guagliardo, Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), University of Edinburgh, Value from Data (VALDA ), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria), and This work was partly supported by EPSRC grants M025268 and N023056.

Subjects
Predicate logic, Linguistics and Language, SQL, Theoretical computer science, Interpretation (logic), Computer science, Incomplete Information, Logics, Predicate (mathematical logic), Extension (predicate logic), Propositional calculus, Language and Linguistics, First-order logic, Many-valued logics, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, Null (SQL), Artificial Intelligence, Truth value, [INFO]Computer Science [cs], Incomplete information
Abstract

International audience; One of the most common scenarios of handling incomplete information occurs in relational databases. They describe in-complete knowledge with three truth values, using Kleene’s logic for propositional formulae and a rather peculiar exten-sion to predicate calculus. This design by a committee from several decades ago is now part of the standard adopted by vendors of database management systems. But is it really the right way to handle incompleteness in propositional and pred-icate logics?Our goal is to answer this question. Using an epistemic ap-proach, we first characterize possible levels of partial knowl-edge about propositions, which leads to six truth values. We impose rationality conditions on the semantics of the connec-tives of the propositional logic, and prove that Kleene’s logic is the maximal sublogic to which the standard optimization rules apply, thereby justifying this design choice. For exten-sions to predicate logic, however, we show that the additional truth values are not necessary: every many-valued extension of first-order logic over databases with incomplete informa-tion represented by null values is no more powerful than the usual two-valued logic with the standard Boolean interpreta-tion of the connectives. We use this observation to analyze the logic underlying SQL query evaluation, and conclude that the many-valued extension for handling incompleteness does not add any expressiveness to it.

Published
2022
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22. Relational and XML Data Exchange

Author

Marcelo Arenas, Pablo Barcelo, Leonid Libkin, Filip Murlak, Marcelo Arenas, Pablo Barcelo, Leonid Libkin, and Filip Murlak

Subjects
Data structures (Computer science), Information theory
Abstract

Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target. Such a target instance should correctly represent information from the source instance under the constraints imposed by the target schema, and it should allow one to evaluate queries on the target instance in a way that is semantically consistent with the source data. Data exchange is an old problem that re-emerged as an active research topic recently, due to the increased need for exchange of data in various formats, often in e-business applications. In this lecture, we give an overview of the basic concepts of data exchange in both relational and XML contexts. We give examples of data exchange problems, and we introduce the main tasks that need to addressed. We then discuss relational data exchange, concentrating on issues such as relational schema mappings, materializing target instances (including canonical solutions and cores), query answering, and query rewriting. After that, we discuss metadata management, i.e., handling schema mappings themselves. We pay particular attention to operations on schema mappings, such as composition and inverse. Finally, we describe both data exchange and metadata management in the context of XML. We use mappings based on transforming tree patterns, and we show that they lead to a host of new problems that did not arise in the relational case, but they need to be addressed for XML. These include consistency issues for mappings and schemas, as well as imposing tighter restrictions on mappings and queries to achieve tractable query answering in data exchange. Table of Contents: Overview / Relational Mappings and Data Exchange / Metadata Management / XML Mappings and Data Exchange

Published
2022

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