Schema Mapping Evolution Through Composition and Inversion

Fagin, Ronald; Kolaitis, Phokion G.; Popa, Lucian; Tan, Wang Chiew

doi:10.1007/978-3-642-16518-4_7

Cited by 33 publications

(27 citation statements)

References 22 publications

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“…To preserve the structure of the raw data stored in the lake, we adopt two virtual transformation operations, Composition μ and Decomposition γ, to work on the schema of the raw data rather than on the data themselves. The virtual transformation operations are based on [46,47], and they allow SemLinker to virtually map an attribute in a source schema to a property in the global ontology. A, A = (a 1 , a 2 , .…”

Section: Virtual Transformation Of Source Attributementioning

confidence: 99%

SemLinker: automating big data integration for casual users

Alrehamy

Walker

2018

J Big Data

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Section: Virtual Transformation Of Source Attributementioning

confidence: 99%

SemLinker: automating big data integration for casual users

Alrehamy

Walker

2018

J Big Data

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“…Various application settings for composition have been proposed (e.g., schema evolution [29]), including mapping optimization [30]. With this paper, we contribute with a further application of mapping compositions, analyzing the internal characteristics of operators, discussing their composability, and actively using them in the optimization.…”

Section: Bridging the Gap Between Specification And Executionmentioning

confidence: 99%

Executable schema mappings for statistical data processing

Atzeni

Bellomarini

Bugiotti

et al. 2017

Distrib Parallel Databases

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Data processing is the core of any statistical information system. Statisticians are interested in specifying transformations and manipulations of data at a high level, in terms of entities of statistical models. We illustrate here a proposal where a high-level language, EXL, is used for the declarative specification of statistical programs, and a translation into executable form in various target systems is available. The language is based on the theory of schema mappings, in particular those defined by a specific class of tgds, which we actually use to optimize user programs and facilitate the translation towards several target systems. The characteristics of such class guarantee good tractability properties and the applicability in Big Data settings. A concrete implementation, EXLEngine, has been carried out and is currently used at the Bank of Italy.

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“…We shall refer to such constraints as extended LAV. In a number of papers, including [ABFL04, FKMP05, Fag07, FKPT11], our notion of "extended LAV" is called simply "LAV", and in [FKPT11], our notion of "LAV" is called "strict LAV". Note also that there is yet another notion of "LAV" in the literature, which is defined even more strictly than our definition, by requiring that all variables in x appear in the conclusion.…”

Section: Preliminariesmentioning

confidence: 99%

Local transformations and conjunctive-query equivalence

Fagin

Kolaitis

2012

Proceedings of the 31st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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Over the past several decades, the study of conjunctive queries has occupied a central place in the theory and practice of database systems. In recent years, conjunctive queries have played a prominent role in the design and use of schema mappings for data integration and data exchange tasks. In this paper, we investigate several different aspects of conjunctive-query equivalence in the context of schema mappings and data exchange.In the first part of the paper, we introduce and study a notion of a local transformation between database instances that is based on conjunctive-query equivalence. We show that the chase procedure for GLAV mappings (that is, schema mappings specified by sourceto-target tuple-generating dependencies) is a local transformation with respect to conjunctive-query equivalence. This means that the chase procedure preserves bounded conjunctive-query equivalence, that is, if two source instances are indistinguishable using conjunctive queries of a sufficiently large size, then the target instances obtained by chasing these two source instances are also indistinguishable using conjunctive queries of a given size. Moreover, we obtain polynomial bounds on the level of indistinguishability between source instances needed to guarantee indistinguishability between the target instances produced by the chase. The locality of the chase extends to schema mappings specified by a secondorder tuple-generating dependency (SO tgd), but does not hold for schema mappings whose specification includes target constraints.In the second part of the paper, we take a closer look at the composition of two GLAV mappings. In particular, we break GLAV mappings into a small number of well-studied classes (including LAV and GAV), and complete the picture as to when the composition of schema mappings from these various classes can be guaranteed to be a GLAV mapping, and when they can be guaranteed to be conjunctive-query equivalent to a GLAV mapping.We also show that the following problem is decidable: given a schema mapping specified by an SO tgd and a GLAV mapping, are they conjunctive-query equivalent? In contrast, the following problem is known to be undecidable: given a schema mapping specified by an SO tgd and a GLAV mapping, are they logically equivalent? Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

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Schema Mapping Evolution Through Composition and Inversion

Cited by 33 publications

References 22 publications

SemLinker: automating big data integration for casual users

SemLinker: automating big data integration for casual users

Executable schema mappings for statistical data processing

Local transformations and conjunctive-query equivalence

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