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Ronald Fagin, Phokion Kolaitis, Lucian Popa, W. Tan (2007)
Quasi-inverses of schema mappingsACM Trans. Database Syst., 33
Ronald Fagin, Phokion Kolaitis, Lucian Popa, W. Tan (2004)
Composing schema mappings: second-order dependencies to the rescueACM Trans. Database Syst., 30
Renée Miller, Y. Ioannidis, R. Ramakrishnan (1994)
Schema equivalence in heterogeneous systems: bridging theory and practiceInf. Syst., 19
M. Lenzerini (2002)
Data integration: a theoretical perspective
M. Arenas, Jorge Pérez, Juan Reutter, Cristian Riveros (2010)
Foundations of schema mapping management
A. Doan, A. Halevy, Z. Ives (2012)
Principles of Data Integration
Ronald Fagin (2006)
Inverting schema mappings
S. Abiteboul, R. Hull, V. Vianu (1995)
Foundations of Databases: The Logical Level
M. Arenas, P. Barceló, L. Libkin, Filip Murlak (2014)
Foundations of Data Exchange
S. Cosmadakis, P. Kanellakis (1985)
Equational theories and database constraints
M. Arenas, P. Barceló, L. Libkin, Filip Murlak (2014)
Foundations of Data Exchange: Preface
Emanuel Sallinger (2013)
Reasoning about Schema Mappings
Diego Calvanese, Giuseppe Giacomo, M. Lenzerini, Moshe Vardi (2011)
Simplifying schema mappings
Adrian Onet (2013)
The Chase Procedure and its Applications in Data Exchange
S. Homer, A. Selman (2011)
Computability and Complexity Theory, Second Edition
Phokion Kolaitis (2005)
Schema mappings, data exchange, and metadata management
S. Homer, A. Selman (2001)
Computability and Complexity Theory
Ronald Fagin, Phokion Kolaitis, Lucian Popa, W. Tan (2009)
Reverse data exchange: coping with nulls
Moreover, by the semantics of schema mappings, for every instance I ∈ sat (S 1 , Σ 1 ), every solution J i of I under M 12 is in sat
Ronald Fagin, Phokion Kolaitis, Renée Miller, Lucian Popa (2003)
Data exchange: semantics and query answering
FaginRonald, G. KolaitisPhokion, PopaLucian, TanWang-Chiew (2005)
Composing schema mappingsACM Transactions on Database Systems
Renée Miller, Y. Ioannidis, R. Ramakrishnan (1993)
The Use of Information Capacity in Schema Integration and Translation
In this paper, we introduce and study schema mapping coverage for relational databases. Given a relational schema mapping in the presence of both source dependencies and target dependencies, the coverage problem is to decide and describe which source instances have solutions under the mapping. Our main motivation is to describe limitations of schema mappings and hence to effectively determine if the mappings can fulfill their user expectations for the given tasks. We first propose using database dependencies to model user expectations of schema mappings. Then we formally define the notion of schema mapping coverage and propose using a set of dependencies in the source schema language to represent coverage. We look into how target dependencies indirectly enforce dependencies via mapping specifications on source instances and thus determine which source instances have solutions. We prove that the problem of computing schema mapping coverage is undecidable in general. We present algorithms for computing coverage for schema mappings where both the source dependencies and the target dependencies consist of functional dependencies and acyclic inclusion dependencies. Schema mapping coverage describes the ability to map source instances to target instances for mappings, it provides a way to describe limitations of schema mappings, which can be especially useful for mapping design and evolution.
Annals of Mathematics and Artificial Intelligence – Springer Journals
Published: Jan 2, 2021
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