Data exchange: semantics and query answering

By Ronald Fagin, Phokion G. Kolaitis, Renée J. Miller, Lucian Popa. Theoretical Computer Science, 2003

1,519 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/9a9e600a263303836b975c9040d80a98f4268cc6

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Citations in the corpus

Composing Mappings Among Data Sources (2003, methodology): “This paper considers the mapping composition problem for the GLAV formalism [9, 8, 12], which combines the practical bene(cid:12)ts of both GAV and LAV.”
On rules with existential variables: Walking the decidability line (2011, methodology): “An example of a prominent application is data exchange, where a building block is the notion of schema mapping, which specifies the relationships between heterogeneous database schemas [30].”
Bidirectional Transformations: A Cross-Discipline Perspective (2009, background): “The constraints may be specified in whatever query or logical language suits the models, such as tuplegenerating dependencies of first-order logic [38] or second-order logic [37] for relations or tree-based pattern matching for XML [7].”
Description Logic Knowledge and Action Bases (2014, methodology): “…(2010), and Bagheri Hariri et al. (2011), we rely on recent results in data exchange on the ﬁniteness of the chase of tuple-generating dependencies (Fagin, Kolaitis, Miller, & Popa, 2005), though, in our case, we need to extend the approach to deal with (i) incomplete information, (ii) inference…”
Composing schema mappings: second-order dependencies to the rescue (2004, methodology): “In fact, by using techniques from [10], we can show that Σ13 is not equivalent to any finite set of source-to-target tgds.”
Data Complexity of Query Answering in Description Logics (2006, positive): “, in database theory [21] and data exchange [28], the following lemma shows that there is a homomorphism from can(K) to every model of K that preserves the assignment of objects to concepts and relations.”
Data Integration under Integrity Constraints (2004, background): “hand, [ 24 ] deals with tuple-generating dependencies (tgds) and equality-generating dependencies (egds), which together are a more generalcl ass of constraints than the one we consider; on the other hand, the results of [24] are restricted to a class of tgds (the weakly-acyclic tgds) that ensures t”
A general datalog-based framework for tractable query answering over ontologies (2009, neutral): “Categories and Subject Descriptors F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems Computations on discrete structures; H.2.4 [Database Management]: Systems Relational databases, rule-based databases, query processing General Terms Algorithms, Languages”
Provenance in Databases: Why, How, and Where (2009, methodology): “Such values are called labeled nulls [32] and they are automatically generated during the transformation as needed, to enforce the semantics of the tgds.”
Data Lake Management: Challenges and Opportunities (2019, background): “Traditional paradigms for integration, including data federation [21] and data exchange [12], have at best limited value in data lakes.”