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 cleaning on incompleteness and inconsistency with currency reasoning and determination in this paper. We introduce a 4-step framework, named${\sf Imp3C}$, for errors detection and quality improvement in incomplete and inconsistent data without timestamps. We achieve an integrated currency determining method to compute the currency orders among tuples, according to currency constraints. Thus, the inconsistent data and missing values are repaired effectively considering the temporal impact. For both effectiveness and efficiency consideration, we carry out inconsistency repair ahead of incompleteness repair. A currency-related consistency distance metric is defined to measure the similarity between dirty tuples and clean ones more accurately. In addition, currency orders are treated as an important feature in the missing imputation training process. The solution algorithms are introduced in detail with case studies. A thorough experiment on three real-life datasets verifies our method${\sf Imp3C}$improves the performance of data repairing with multiple quality problems.${\sf Imp3C}$outperforms the existing advanced methods, especially in the datasets with complex currency orders.

With the growth of data from various sources, data quality is faced with multiple problems. In this paper, we study the multiple data cleaning on incompleteness and inconsistency with currency reasoning and determination. We introduce a 4-step method, named Imp3C, for error detection and repair in incomplete and inconsistent data without timestamps. We propose an integrated currency determining approach to compute currency order among tuples, thus, the dirty data can be repaired effectively considering the temporal impact. Experiments on three real-life datasets verify that Imp3C improves data repairing performance with multiple quality problems, especially in datasets with complex currency orders.

The great amount of time series generated by machines has enormous value in intelligent industry. Knowledge can be discovered from high-quality time series, and used for production optimization and anomaly detection in industry. However, the original sensors data always contain many errors. This requires a sophisticated cleaning strategy and a well-designed system for industrial data cleaning. Motivated by this, we introduce Cleanits, a system for industrial time series cleaning. It implements an integrated cleaning strategy for detecting and repairing three kinds of errors in industrial time series. We develop reliable data cleaning algorithms, considering features of both industrial time series and domain knowledge. We demonstrate Cleanits with two real datasets from power plants. The system detects and repairs multiple dirty data precisely, and improves the quality of industrial time series effectively. Cleanits has a friendly interface for users, and result visualization along with logs are available during each cleaning process.