  Data quality plays a key role in big data management today. With the
explosive growth of data from a variety of sources, the quality of data is
faced with multiple problems. Motivated by this, we study the multiple data
quality improvement on completeness, consistency and currency in this paper.
For the proposed problem, we introduce a 4-step framework, named Improve3C, for
detection and quality improvement on incomplete and inconsistent data without
timestamps. We compute and achieve a relative currency order among records
derived from given currency constraints, according to which inconsistent and
incomplete data can be repaired effectively considering the temporal impact.
For both effectiveness and efficiency consideration, we carry out inconsistent
repair ahead of incomplete repair. Currency-related consistency distance is
defined to measure the similarity between dirty records and clean ones more
accurately. In addition, currency orders are treated as an important feature in
the training process of incompleteness repair. The solution algorithms are
introduced in detail with examples. A thorough experiment on one real-life data
and a synthetic one verifies that the proposed method can improve the
performance of dirty data cleaning with multiple quality problems which are
hard to be cleaned by the existing approaches effectively.
