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The Recovery of a Schema Mapping: Bringing Exchanged Data Back
 In Proceedings of the 28th ACM Symposium on Principles of Database Systems (PODS
, 2008
"... A schema mapping is a specification that describes how data from a source schema is to be mapped to a target schema. Once the data has been transferred from the source to the target, a natural question is whether one can undo the process and recover the initial data, or at least part of it. In fact, ..."
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Cited by 38 (13 self)
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A schema mapping is a specification that describes how data from a source schema is to be mapped to a target schema. Once the data has been transferred from the source to the target, a natural question is whether one can undo the process and recover the initial data, or at least part of it. In fact, it would be desirable to find a reverse schema mapping from target to source that specifies how to bring the exchanged data back. In this paper, we introduce the notion of a recovery of a schema mapping: it is a reverse mapping M ′ for a mapping M that recovers sound data with respect to M. We further introduce an order relation on recoveries. This allows us to choose mappings that recover the maximum amount of sound information. We call such mappings maximum recoveries. We study maximum recoveries in detail, providing a necessary and sufficient condition for their existence. In particular, we prove that maximum recoveries exist for the class of mappings specified by FOTOCQ sourcetotarget dependencies. This class subsumes the class of sourcetotarget tuplegenerating dependencies used in previous work on data exchange. For the class of mappings specified by FOTOCQ dependencies, we provide an exponentialtime algorithm for computing maximum recoveries, and a simplified version for full dependencies that works in quadratic time. We also characterize the language needed to express maximum recoveries, and we include a detailed comparison with the notion of inverse (and quasiinverse) mapping previously proposed in the data exchange literature. In particular, we show that maximum recoveries strictly generalize inverses. We finally study the complexity of some decision problems related to the notions of recovery and maximum recovery.
Answering NonMonotonic Queries in Relational Data Exchange
"... Relational data exchange deals with translating a relational database instance over some source schema into a relational database instance over some target schema, according to a schema mapping that specifies the relationship between the source data and the target data. Various semantics for answeri ..."
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Relational data exchange deals with translating a relational database instance over some source schema into a relational database instance over some target schema, according to a schema mapping that specifies the relationship between the source data and the target data. Various semantics for answering queries against the target schema exist, each of them suitable for a certain class of queries, and with respect to certain schema mappings. However, for each of these semantics, there are examples that show that it leads to counterintuitive answers, or that it does not respect logical equivalence of schema mappings. In this article, we study query answering semantics for deductive databases in the context of relational data exchange. Furthermore, we propose a new semantics, called GCWA ∗answers semantics, which seems to be wellsuited with respect to a number of schema mappings, including schema mappings defined by sttgds and egds. We show that the GCWA ∗answers semantics coincides with the classical certain answers semantics on monotonic queries, and we further explore the data complexity of computing the GCWA ∗answers to nonmonotonic queries. In particular, we identify a class of schema mappings for which the GCWA ∗answers to universal queries can be computed from the core of the universal solutions in polynomial time (data complexity).
Closed World Data Exchange
, 2011
"... Data exchange deals with translating data structured in some source format into data structured in some target format, given a specification of the relationship between the source and the target and possibly constraints on the target, and answering queries over the target in a way that is semantical ..."
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Cited by 6 (4 self)
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Data exchange deals with translating data structured in some source format into data structured in some target format, given a specification of the relationship between the source and the target and possibly constraints on the target, and answering queries over the target in a way that is semantically consistent with the information in the source. Theoretical foundations of data exchange have been actively explored recently. It was also noticed that the standard semantics for query answering in data exchange may lead to counterintuitive or anomalous answers. In the present paper, we explain that this behavior is due to the fact that solutions can contain “invented” information (i.e., information that is not related to the source instance), and that the presence of incomplete information in target instances has been ignored. In particular, proper query evaluation techniques for databases with nulls have not been used, and the distinction between closed and open world semantics has not been made. We present a concept of solutions, called CWAsolutions, that is based on the closed world assumption. For data exchange settings without constraints on the target, the space of CWAsolutions has two extreme points: the canonical universal solution (the “maximal ” CWAsolution) and the core of the universal solutions (the “minimal” CWAsolution), both of them well studied in data exchange. In the presence of constraints on the target, the core of the universal solutions is still the “minimal” CWAsolution, but there may be no unique “maximal” CWAsolution. We show how to define the semantics of query answering taking into account incomplete information, and show that some of the wellknown anomalies go away with the new semantics. The paper also contains results on the complexity of query answering, upper approximations to queries (maybeanswers), and various extensions.
Semantics for NonMonotone Queries in Data Exchange and Data Integration
"... A fundamental question in data exchange and data integration is how to answer queries that are posed against the target schema, or the global schema, respectively. While the certain answers semantics has proved to be adequate for answering monotone queries, the question concerning an appropriate sem ..."
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Cited by 1 (0 self)
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A fundamental question in data exchange and data integration is how to answer queries that are posed against the target schema, or the global schema, respectively. While the certain answers semantics has proved to be adequate for answering monotone queries, the question concerning an appropriate semantics for nonmonotone queries turned out to be more difficult. This article surveys approaches and semantics for answering nonmonotone queries in data exchange and data integration.
Towards General Representability in Knowledge Exchange
"... In data exchange, one is typically given a source database instance and a mapping between a source schema and a target schema. The goal then is to materialize a target database instance that corresponds to the source instance and the mapping. In this setting source data is explicitly given, that is, ..."
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In data exchange, one is typically given a source database instance and a mapping between a source schema and a target schema. The goal then is to materialize a target database instance that corresponds to the source instance and the mapping. In this setting source data is explicitly given, that is, every fact that is true in it is explicitly mentioned