Nested dependencies

Kolaitis, Phokion G.; Pichler, Reinhard; Sallinger, Emanuel; Savenkov, Vadim

doi:10.1145/2594538.2594544

Cited by 9 publications

(10 citation statements)

References 21 publications

(30 reference statements)

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“…We will propose a sufficient condition and show that it reveals the native structural characterization of plain SO tgds rewritable to nested tgds, which is more general than the conditions given in [6]. There is also a theoretical tool given in [21] to tell when a plain SO tgd does not have equivalent nested tgds, but it is difficult to be directly applied in an algorithmic approach.…”

Section: Motivation and Problem Definitionmentioning

confidence: 98%

“…Although SO tgds and plain SO tgds have stronger expressive power, they are less desirable specifications in practice compared to dependencies with FO semantics. To begin with, FO and SO logics correspond to algorithmic behaviors with different complexities [21]. For example, in a data exchange system the source data needs to be checked whether it satisfies the given mappings, which corresponds to the problem of model-checking.…”

Section: Motivation and Problem Definitionmentioning

confidence: 99%

“…For example, in a data exchange system the source data needs to be checked whether it satisfies the given mappings, which corresponds to the problem of model-checking. It is proven that the data complexity of the model checking problem for plain SO tgds is NP-complete while the same problem is merely LOGSPACE for nested tgds [25,21]. Moreover, some reasoning tasks are undecidable for SO tgds, such as logical equivalence [11], which plays a fundamental role in mapping optimization [9].…”

Section: Motivation and Problem Definitionmentioning

confidence: 99%

“…Yet, logical equivalence for nested tgds is decidable. In addition, FO tgds are more user-friendly and can be easily translated to SQL [24,21].…”

Section: Motivation and Problem Definitionmentioning

confidence: 99%

Section: Schema and Schema Mappingmentioning

confidence: 99%

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Rewriting of plain SO tgds into nested tgds

Hai

Quix

2019

Proc. VLDB Endow.

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Schema mappings express the relationships between sources in data interoperability scenarios and can be expressed in various formalisms. Source-to-target tuple-generating dependencies (s-t tgds) can be easily used for data transformation or query rewriting tasks. Second-order tgds (SO tgds) are more expressive as they can also represent the composition and inversion of s-t tgds. Yet, the expressive power of SO tgds comes with the problem of undecidability for some reasoning tasks. Nested tgds and plain SO tgds are mapping languages that are between s-t tgds and SO tgds in terms of expressivity, and their properties have been studied in the recent years. Nested tgds are less expressive than plain SO tgds, but the logical equivalence problem for nested tgds is decidable. However, a detailed characterization of plain SO tgds that have an equivalent nested tgd is missing. In this paper, we present an algorithmic solution for translating plain SO tgds into nested tgds. The algorithm computes one or more nested tgds, if a given plain SO tgd is rewritable. Furthermore, we are able to give a detailed characterization of those plain SO tgds for which an equivalent nested tgd exists, based on the structural properties of the source predicates and Skolem functions in the plain SO tgd. In the evaluation, we show that our algorithm covers a larger subset of plain SO tgds than previous approaches and that a rewriting can be computed efficiently although the algorithm has the exponential complexity.

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Section: Motivation and Problem Definitionmentioning

confidence: 98%

Section: Motivation and Problem Definitionmentioning

confidence: 99%

Section: Motivation and Problem Definitionmentioning

confidence: 99%

“…Yet, logical equivalence for nested tgds is decidable. In addition, FO tgds are more user-friendly and can be easily translated to SQL [24,21].…”

Section: Motivation and Problem Definitionmentioning

confidence: 99%

Section: Schema and Schema Mappingmentioning

confidence: 99%

See 3 more Smart Citations

Rewriting of plain SO tgds into nested tgds

Hai

Quix

2019

Proc. VLDB Endow.

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Data Science with Vadalog: Bridging Machine Learning and Reasoning

Bellomarini

Fayzrakhmanov

Gottlob

et al. 2018

Model and Data Engineering

Self Cite

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Following the recent successful examples of large technology companies, many modern enterprises seek to build knowledge graphs to provide a unified view of corporate knowledge and to draw deep insights using machine learning and logical reasoning. There is currently a perceived disconnect between the traditional approaches for data science, typically based on machine learning and statistical modelling, and systems for reasoning with domain knowledge. In this paper we present a state-of-the-art Knowledge Graph Management System, Vadalog, which delivers highly expressive and efficient logical reasoning and provides seamless integration with modern data science toolkits, such as the Jupyter platform. We demonstrate how to use Vadalog to perform traditional data wrangling tasks, as well as complex logical and probabilistic reasoning. We argue that this is a significant step forward towards combining machine learning and reasoning in data science.

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