With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

Errors are prevalent in time series data, which is particularly common in the industrial field.
Data with errors could not be stored in the database, which results in the loss of data assets …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …

With the continued deployment of the Internet of Things (IoT), increasing volumes of devices
are being deployed that emit massive spatially referenced data. Due in part to the dynamic …

The quality assurance of the knowledge graph is a prerequisite for various knowledge-
driven applications. We propose KGClean, a novel cleaning framework powered by …

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue
to emerge. Data quality issues caused by near-duplicate videos are becoming more and …

The research on fair machine learning (ML) has been growing due to the high demand for
unbiased and fair ML models for objective decision-making. Most of this research has been …

When retrieving data from IoT devices, errors in time series data often occur due to
interference. Data with errors cannot be processed or saved in databases and warehouses …

Duplicate detection identifies multiple records in a dataset that represent the same real-
world object. Many such approaches exist, both in research and in industry. To investigate …

The rise of GPS-equipped mobile devices has led to the emergence of big trajectory data.
The collected raw data usually contain errors and anomalies information caused by device …