  Event data are prevalent in diverse domains such as financial trading,
business workflows and industrial IoT nowadays. An event is often characterized
by several attributes denoting the meaning associated with the corresponding
occurrence time/duration. From traditional operational systems in enterprises
to online systems for Web services, event data is generated from physical world
uninterruptedly. However, due to the variety and veracity features of Big data,
event data generated from heterogeneous and dirty sources could have very
different event representations and data quality issues. In this work, we
summarize several typical works on studying data quality issues of event data,
including: (1) event matching, (2) event error detection, (3) event data
repair, and (4) approximate pattern matching.
