Process Mining allows organizations to obtain actual business process models from event
logs (discovery), to compare the event log or the resulting process model in the discovery …

Time series are all around in real-world applications. However, unexpected accidents for
example broken sensors or missing of the signals will cause missing values in time series …

Besides the conventional schema-oriented tasks, data dependencies are recently revisited
for data quality applications, such as violation detection, data repairing and record matching …

Real-life event logs, reflecting the actual executions of complex business processes, are
faced with numerous data quality issues. Extensive data sanity checks and pre-processing …

Due to the development of internet technology and computer science, data is exploding at
an exponential rate. Big data brings us new opportunities and challenges. On the one hand …

Data quality issues have been widely recognized in IoT data, and prevent the downstream
applications. In this tutorial, we review the state-of-the-art techniques for IoT data quality …

Event data are often dirty owing to various recording conventions or simply system errors.
These errors may cause serious damage to real applications, such as inaccurate …

To cope with challenges such as tightening budgets and increased care needs, healthcare
organizations are becoming increasingly aware of the need to understand their processes in …

As data volume grows extensively, data profiling helps to extract metadata of large-scale
data. However, one kind of metadata, order statistics, is difficult to be computed because …

Process mining is a discipline that analyzes real event data extracted from information
systems that support a business process to construct as-is process models and detect …