Mapping and cleaning

Geerts, Floris; Mecca, Giansalvatore; Papotti, Paolo; Santoro, Donatello

doi:10.1109/icde.2014.6816654

Cited by 35 publications

(41 citation statements)

References 24 publications

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“…Line 13 ensures that µ is correctly updated even if several EGDs are applied in a single iteration. Since both EGDs and TGDs can be applied in an iteration, both I and N must be updated at the iteration end (lines [14][15][16]. When h(x i ) and h(x j ) are both labeled nulls in line 10, we can replace either value with the other.…”

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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show abstract

“…Again, this is an important example but only evaluates one dimension. Geerts et al [2014] address two data quality issues: transforming data using schema mappings and fixing data conflicts and inconsistencies. The example mapping provided describes a source database that contains data on Treatments and Physicians and a second target database detailing Prescriptions and Doctors.…”

Section: Related Workmentioning

confidence: 99%

Qual

Baillie

Edwards

Pignotti

2015

J. Data and Information Quality

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In this article, we present a model for quality assessment over linked data. This model has been designed to align with emerging standards for provenance on the Web to enable agents to reason about data provenance when performing quality assessment. The model also enables quality assessment provenance to be represented, thus allowing agents to make decisions about reuse of existing assessments. We also discuss the development of an OWL ontology as part of a software framework to support reasoning about data quality and assessment reuse. Finally, we evaluate this framework using two real-world case studies derived from transport and invasive-species monitoring applications.

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“…Despite the fact that ++Spicy is based on pure SQL, we have shown that our custom chase exhibits better performance on large instances in our tested scenarios. The Llunatic mapping and cleaning system [7] is tackling the problem of mapping and cleaning in the same chase step. The Llunatic chase efficiently covers a range of scenarios, such as repairing source data by also exploiting user feedback, that are not covered in our algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Many existing approaches have aimed at rewriting the dependencies, so as to further leverage the power of an external, general evaluation engine, such as a traditional RDBMS [17,15] or a Datalog engine [12,10]. Alternatively, custom chase engines have been used and optimized for computing DE solutions [7,16].…”

Section: Introductionmentioning

confidence: 99%

Functional Dependencies Unleashed for Scalable Data Exchange

Bonifati

Ileana

Linardi

2016

Proceedings of the 28th International Conference on Scientific and Statistical Database Management

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International audienceWe address the problem of efficiently evaluating target functional dependencies (fds) in the Data Exchange (DE) process. Target fds naturally occur in many DE scenarios, including the ones in Life Sciences in which multiple source relations need to be structured under a constrained target schema. However, despite their wide use, target fds' evaluation is still a bottleneck in the state-of-the-art DE engines. Systems relying on an all-SQL approach typically do not support target fds unless additional information is provided. Alternatively, DE engines that do include these dependencies typically pay the price of a significant drop in performance and scalability. In this paper, we present a novel chase-based algorithm that can efficiently handle arbitrary fds on the target. Our approach essentially relies on exploiting the interactions between source-to-target (s-t) tuple-generating dependencies (tgds) and target fds. This allows us to tame the size of the intermediate chase results, by playing on a careful ordering of chase steps interleaving fds and (chosen) tgds. As a direct consequence, we importantly diminish the fd application scope, often a central cause of the dramatic overhead induced by target fds. Moreover, reasoning on dependency interaction further leads us to interesting parallelization opportunities, yielding additional scalability gains. We provide a proof-of-concept implementation of our chase-based algorithm and an experimental study aimed at gauging its scalability and efficiency. Finally, we empirically compare with the latest DE engines, and show that our algorithm outperforms them

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Mapping and cleaning

Cited by 35 publications

References 24 publications

Benchmarking the Chase

Benchmarking the Chase

Qual

Functional Dependencies Unleashed for Scalable Data Exchange

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