2010 22nd IEEE International Conference on Tools With Artificial Intelligence 2010
DOI: 10.1109/ictai.2010.43
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An Efficient and Robust Approach for Discovering Data Quality Rules

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Cited by 20 publications
(13 citation statements)
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“…The majority of studies in the area of DQ originate from the database context [2,3] and management research communities. According to [17], DQ is not an easy concept to define.…”
Section: Data Quality Quality Dimensions and Metricsmentioning
confidence: 99%
See 1 more Smart Citation
“…The majority of studies in the area of DQ originate from the database context [2,3] and management research communities. According to [17], DQ is not an easy concept to define.…”
Section: Data Quality Quality Dimensions and Metricsmentioning
confidence: 99%
“…Data Quality has been an active and attractive research area for several years [2,3]. In the context of Big Data, quality assessment processes are hard to implement, since they are time-and cost-consuming, especially for the pre-processing activities.…”
mentioning
confidence: 99%
“…However, poor data quality can occur along several dimensions [29]. These dimensions of data quality attribute as shown in figure (2) include; intrinsic (accuracy, consistency, reliability, integrity, and redundancy dimension), accessibility (availability dimension), contextual (relevancy, freshness, validity, completeness, and Scalability dimension), and representational (business purpose understandability and data sources understandability dimension) [28,30,31].…”
Section: Data Qualitymentioning
confidence: 99%
“…These dimensions of data quality attribute as shown in figure (2) include; intrinsic (accuracy, consistency, reliability, integrity, and redundancy dimension), accessibility (availability dimension), contextual (relevancy, freshness, validity, completeness, and Scalability dimension), and representational (business purpose understandability and data sources understandability dimension) [28,30,31]. Furthermore, data quality problems produce at various stages of CDWH development; data integration & data profiling, Data staging and ETL, and DWH modeling & schema design [29,30]. Additionally, these data quality problems must be determined and solved to enhance the quality of data.…”
Section: Data Qualitymentioning
confidence: 99%
“…When such dependencies are violated, it results in inconsistency [22]. There are single functional dependence inconsistency, multi functional dependencies inconsistency [22], and conditional functional dependencies [8,33]. Another important tool in building rational agents is inheritance that offers an effective logical reasoning for abstraction, classification and generalization in hierarchies of concepts or objects [2].…”
Section: Inconsistencymentioning
confidence: 99%