Towards a Compositional Semantic Account of Data Quality Attributes

Jiang, Lei; Borgida, Alex; Mylopoulos, John

doi:10.1007/978-3-540-87877-3_6

Cited by 13 publications

(24 citation statements)

References 17 publications

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“…The quality of data cannot be assessed without contextual knowledge about the production or the use of data. Actually, the notion of data quality is based on the degree in which the data fits or fulfills a form of usage [1], [13]. As a consequence, the quality of data depends on their use context.…”

Section: Introductionmentioning

confidence: 99%

Extending contexts with ontologies for multidimensional data quality assessment

Milani

Bertossi

Ariyan

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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Data quality and data cleaning are context dependent activities. Starting from this observation, in previous work a context model for the assessment of the quality of a database instance was proposed. In that framework, the context takes the form of a possibly virtual database or data integration system into which a database instance under quality assessment is mapped, for additional analysis and processing, enabling quality assessment. In this work we extend contexts with dimensions, and by doing so, we make possible a multidimensional assessment of data quality assessment. Multidimensional contexts are represented as ontologies written in Datalog±. We use this language for representing dimensional constraints, and dimensional rules, and also for doing query answering based on dimensional navigation, which becomes an important auxiliary activity in the assessment of data. We show ideas and mechanisms by means of examples. TABLE I Measurements Time Patient Value

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Section: Introductionmentioning

confidence: 99%

Extending contexts with ontologies for multidimensional data quality assessment

Milani

Bertossi

Ariyan

2014

2014 IEEE 30th International Conference on Data Engineering Workshops

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“…By detailed analysis of the different quality indicators shown in [15][16][17][18], we adopt the following metric as the data quality evaluation for the WSNs: data volume, completeness, time correlation, and correctness. Let q v , q c , q t , and q a represent the corresponding quality indicators of dataset D.…”

Section: Network Model and Problemmentioning

confidence: 99%

Data quality analysis and cleaning strategy for wireless sensor networks

Cheng

Feng

Shi

et al. 2018

J Wireless Com Network

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The quality of data in wireless sensor networks has a significant impact on decision support, and data cleaning is an effective way to improve data quality. However, if the data cleaning strategies are not correctly designed, it might result in an unsatisfactory cleaning effect with increased system cleaning costs. Initially, data quality evaluation indicators and their measurement methods in wireless sensor networks were introduced. We then explored the impact of relationship between different indicators which are used in the quality assessment. Finally, data cleaning strategy for wireless sensor networks based on the relationship between data quality indicators was proposed by comparing and analyzing data cleaning schemes with different orders. The experimental results showed that the proposed data cleaning strategy can effectively improve data availability and have a better cleaning effect in wireless sensor networks for the same cleaning cost.

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“…Analysis of mappings has become an important new topic with work on verification of mappings [14], mapping quality [15,32], and mapping optimization [20], to name just a few.…”

Section: Related Workmentioning

confidence: 99%

Clio: Schema Mapping Creation and Data Exchange

Fagin¹,

Haas²,

Hernández³

et al. 2009

Conceptual Modeling: Foundations and Applications

165

118

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Abstract.The Clio project provides tools that vastly simplify information integration. Information integration requires data conversions to bring data in different representations into a common form. Key contributions of Clio are the definition of non-procedural schema mappings to describe the relationship between data in heterogeneous schemas, a new paradigm in which we view the mapping creation process as one of query discovery, and algorithms for automatically generating queries for data transformation from the mappings. Clio provides algorithms to address the needs of two major information integration problems, namely, data integration and data exchange. In this chapter, we present our algorithms for both schema mapping creation via query discovery, and for query generation for data exchange. These algorithms can be used in pure relational, pure XML, nested relational, or mixed relational and nested contexts.

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Towards a Compositional Semantic Account of Data Quality Attributes

Cited by 13 publications

References 17 publications

Extending contexts with ontologies for multidimensional data quality assessment

Extending contexts with ontologies for multidimensional data quality assessment

Data quality analysis and cleaning strategy for wireless sensor networks

Clio: Schema Mapping Creation and Data Exchange

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