事件数据质量研究综述
Event Data Quality: A Survey
摘要：
如今，事件数据在金融交易、业务工作流程和工业物联网等不同领域都很普遍。一个事件通常由几个属性来表征，这些属性表示与相应的发生时间/持续时间相关联的含义。从传统的企业业务系统到Web服务的在线系统，事件数据不间断地从物理世界产生。然而，由于大数据的多样性和准确性特征，从异构和脏源生成的事件数据可能具有非常不同的事件表示和数据质量问题。本文总结了研究事件数据质量问题的几个典型工作，包括：（1）事件匹配，（2）事件错误检测，（3）事件数据修复，（4）近似模式匹配。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
