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Data quality plays a key role in big data management today. With theexplosive growth of data from a variety of sources, the quality of data isfaced with multiple problems. Motivated by this, we study the multiple dataquality improvement on completeness, consistency and currency in this paper.For the proposed problem, we introduce a 4-step framework, named Improve3C, fordetection and quality improvement on incomplete and inconsistent data withouttimestamps. We compute and achieve a relative currency order among recordsderived from given currency constraints, according to which inconsistent andincomplete data can be repaired effectively considering the temporal impact.For both effectiveness and efficiency consideration, we carry out inconsistentrepair ahead of incomplete repair. Currency-related consistency distance isdefined to measure the similarity between dirty records and clean ones moreaccurately. In addition, currency orders are treated as an important feature inthe training process of incompleteness repair. The solution algorithms areintroduced in detail with examples. A thorough experiment on one real-life dataand a synthetic one verifies that the proposed method can improve theperformance of dirty data cleaning with multiple quality problems which arehard to be cleaned by the existing approaches effectively.

Improve3C: Data Cleaning on Consistency and Completeness with Currency