Data cleaning is considered as an effective approach of improving data quality in order to
help practitioners and researchers be devoted to downstream analysis and decision-making …

Billions of data points are generated by devices equipped with thousands of sensors, leading
to significant data quality issues in time series data. These errors not only complicate time …

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 …

The widespread existence of time series data in information systems poses significant
challenges to data cleaning due to its quality issues, particularly the complex interdependencies …

The explosive growth of time series captured by sensors in industrial pipelines gives rise to
the flourish of intelligent industry. Exploiting the value of these time series is conductive to …

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 …

Intelligent devices often produce time series data that suffer from significant data quality
issues. While the utilization of data dependency in error detection and data repair has been …

With the development of data market, data resources play a key role as the part of business
resources. However, existing data markets are too simple to reveal the real data values in …

Assessing data quality through Functional Depen-dencies (FDs) is a crucial aspect of data
governance. However, with the diverse range of data sources and the exponential growth in …

Time series classification is a crucial task in time series data analysis. Sensor-collected time
series data have been observed to often contain missing values due to equipment failures, …