BAGH -比较研究
BAGH -- Comparative study
摘要：
流程挖掘是近十年来出现的一个新的研究方向，它主要是利用事件日志和数据来分析流程。业务流程操作的信息系统集成度的提高为创新的数据分析方法提供了基础。流程挖掘与数据采掘有着密切的联系，因此它可以将商业智能方法与业务流程管理联系起来。它专注于端到端流程，并且由于事件数据的可用性不断增长以及新的流程发现和一致性检查技术而成为可能。过程挖掘的目的是通过从信息系统中的事件日志中提取知识来发现、监控和改进真实的过程。所发现的流程模型可以用于各种分析目的。许多公司已经采用流程感知信息系统来以某种形式支持他们的业务流程。这些系统通常具有与实际业务流程执行相关的日志事件。对流程感知信息系统执行日志的正确分析可以产生重要的知识，并帮助组织提高其服务质量。本文回顾和比较各种流程挖掘算法的输入参数的基础上，使用的技术和它们产生的输出。
英文摘要：
Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the basis for innovative data analysis approaches. Process mining has the strong relationship between with data mining so that it enables the bond between business intelligence approach and business process management. It focuses on end to end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs readily available in todays information systems. The discovered process models can be used for a variety of analysis purposes. Many companies have adopted Process aware Information Systems for supporting their business processes in some form. These systems typically have their log events related to the actual business process executions. Proper analysis of Process Aware Information Systems execution logs can yield important knowledge and help organizations improve the quality of their services. This paper reviews and compares various process mining algorithms based on their input parameters, the techniques used and the output generated by them.
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。
