Data Quality Aware Queries in Collaborative Information Systems

Yeganeh, Naiem Khodabandehloo; Sadiq, Shazia; Deng, Ke; Zhou, Xiaofang

doi:10.1007/978-3-642-00672-2_6

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…A query language extension method has been used to provide additional functionality for the integrated applications. A framework for data quality aware queries is presented by extending SQL query language [17] while privacy aware querying language has been designed for preserving privacy in distributed query evaluation [18]. To the best of our knowledge, there is no querying system available for data concern aware querying to integrate data services.…”

Section: Structure and Summary Of Resultsmentioning

confidence: 99%

Incorporating Data Concerns into Query Languages for Data Services

Ali

Pichler

Truong

et al. 2012

Enterprise Information Systems

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Abstract. More and more organizations provide their data on the web via data services -also referred to as Data as a Service (DaaS). Data services combine the strength of database systems and query languages on the one hand with the benefits of service-oriented architecture on the other hand. Data services are increasingly used for data integration. The data provided via data services is often associated with data concerns like privacy, licensing, pricing, quality of data, etc. Hence, data integration tools not only have to mitigate the heterogeneity in data formats and query languages. In addition, also the various data concerns should be preserved when data is published and utilized. Moreover, data service selection and data selection should be based on these data concerns. Current Data Integration systems using data services lack the ability to preserve data concerns while querying multiple services in an integrated environment. In this paper, we design a new querying system which takes data concerns into account. To this end we discuss several models of data concern aware querying and select the best suited one for our system. We describe a querying system where data concern awareness is integrated directly into the XQuery language. We also report on an implementation and experimental evaluation of this system.

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Section: Structure and Summary Of Resultsmentioning

confidence: 99%

Incorporating Data Concerns into Query Languages for Data Services

Ali

Pichler

Truong

et al. 2012

Enterprise Information Systems

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“…Fourth, data items of the CiDD had a comprehensiveness or coverage [ 8 , 9 ] problem. The CiDD lacks clinical terms frequently used in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the Clinical Data Dictionary (CiDD)

et al. 2010

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ObjectivesThe purpose of the study was to evaluate content coverage and data quality of the Clinical Data Dictionary (CiDD) developed by the Center for Interoperable EHR (CiEHR).MethodsA total of 12,994 terms were collected from 98 clinical forms of a tertiary cancer center hospital with 500 beds. After data cleaning, 9,418 terms were mapped with the data items of the CiDD by the research team, and validated by 30 doctors and nurses at the research hospital.ResultsMapping results were classified into five categories: lexically mapped; semantically mapped; mapped to either a broader term or a narrower term; mapped to more than one term and not mapped. In terms of coverage, out of 9,418 terms, 6,750 (71.7%) terms were mapped; 4,319 (45.9%) terms were lexically mapped; 2,431 (25.8%) were semantically mapped; 281 (3.0%) terms were mapped to a broader term; 43 (0.5%) were mapped to a narrower term; and 550 (5.8%) were mapped to more than one term. In terms of data quality, the CiDD has problems such as errors in concept namingand representation, redundancy in synonyms, inadequate synonyms, and ambiguity in meaning.ConclusionsAlthough the CiDD has terms covering 72% of local clinical terms, the CiDD can be improved by cleaning up errors and redundancies, adding textual definitions or use cases of the concept, and arranging the concepts in a hierarchy.

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“…Secondly, the prevalence of large, multi-source data sets makes it very difficult for data owners and users to have a comprehensive understanding of the data characteristics and properties including data quality levels and targets. In light of these challenges, our current research aims at improving quality awareness [48] in data management systems, especially developing extensions in data management systems that link together data quality specification, measurement, improvement and tracking capabilities.…”

Section: Data Qualitymentioning

confidence: 99%