Core schema mappings: Scalable core computations in data exchange

Mecca, Giansalvatore; Papotti, Paolo; Raunich, Salvatore

doi:10.1016/j.is.2012.03.004

Cited by 18 publications

(16 citation statements)

References 32 publications

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“…The unrestricted Skolem chase [26,38,28] also eliminates line 6, but it also skolemizes TGDs: in each TGD τ of the form (1), each existentially quantified variable y ∈ y is replaced with a function term f ( z) where f is a fresh function symbol and z contains all variables occurring in both the head and the body of τ. The chase then proceeds as in Algorithm 1, but without line 6 and by using the preprocessed τ in line 7.…”

Section: Implementing the Chasementioning

confidence: 99%

Benchmarking the Chase

Benedikt

Konstantinidis

Mecca

et al. 2017

Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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116

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The chase is a family of algorithms used in a number of data management tasks, such as data exchange, answering queries under dependencies, query reformulation with constraints, and data cleaning. It is well established as a theoretical tool for understanding these tasks, and in addition a number of prototype systems have been developed. While individual chase-based systems and particular optimizations of the chase have been experimentally evaluated in the past, we provide the first comprehensive and publicly available benchmark-test infrastructure and a set of test scenarios-for evaluating chase implementations across a wide range of assumptions about the dependencies and the data. We used our benchmark to compare chase-based systems on data exchange and query answering tasks with one another, as well as with systems that can solve similar tasks developed in closely related communities. Our evaluation provided us with a number of new insights concerning the factors that impact the performance of chase implementations.

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Section: Implementing the Chasementioning

confidence: 99%

Benchmarking the Chase

Benedikt

Konstantinidis

Mecca

et al. 2017

Proceedings of the 36th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

Self Cite

116

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“…Also +Spicy [35] delivers a system translating schema mappings into executable queries, not only encoded in SQL but also in XQuery. In this sense, it is one of the first systems to decouple a high-level specification from the executable statements.…”

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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“…In fact, chasing s-t tgds, or even the more general FO-rules [13] is extremely fast. On the contrary, the chase becomes slow whenever it needs to be executed in a violation-by-violation fashion.…”

Section: Scalability and Optimizationsmentioning

confidence: 99%

Mapping and cleaning

Geerts

Mecca

Papotti

et al. 2014

2014 IEEE 30th International Conference on Data Engineering

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We address the challenging and open problem of bringing together two crucial activities in data integration and data quality, i.e., transforming data using schema mappings, and fixing conflicts and inconsistencies using data repairing. This problem is made complex by several factors. First, schema mappings and data repairing have traditionally been considered as separate activities, and research has progressed in a largely independent way in the two fields. Second, the elegant formalizations and the algorithms that have been proposed for both tasks have had mixed fortune in scaling to large databases. In the paper, we introduce a very general notion of a mapping and cleaning scenario that incorporates a wide variety of features, like, for example, user interventions. We develop a new semantics for these scenarios that represents a conservative extension of previous semantics for schema mappings and data repairing. Based on the semantics, we introduce a chase-based algorithm to compute solutions. Appropriate care is devoted to developing a scalable implementation of the chase algorithm. To the best of our knowledge, this is the first general and scalable proposal in this direction.

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Core schema mappings: Scalable core computations in data exchange

Cited by 18 publications

References 32 publications

Benchmarking the Chase

Benchmarking the Chase

Executable schema mappings for statistical data processing

Mapping and cleaning

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