Discovering Patterns using Process Mining

Ishak H. A. Meddah, K. Belkadi
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引用次数: 27

Abstract

Process mining provides an important bridge between data mining and business process analysis, his techniques allow for extracting information from event logs. In general, there are two steps in process mining, correlation definition or discovery and then process inference or composition. Firstly, the authors' work consists to mine small patterns from a log traces of two applications; SKYPE, and VIBER, those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques; The patterns are represented by finite state automaton or their regular expression; The final model is the combination of only two types of small patterns whom are represented by the regular expressions ab* and ab*c*. Secondly, the authors compute these patterns in parallel, and then combine those small patterns using the composition rules, they have two parties the first is the mine, they discover patterns from execution traces and the second is the combination of these small patterns. The patterns mining and the composition is illustrated by the automaton existing techniques. The Execution traces are the different actions effected by users in the SKYPE and VIBER. The results are general and precise. It minimizes the execution time and the loss of information.
使用过程挖掘发现模式
流程挖掘提供了数据挖掘和业务流程分析之间的重要桥梁,他的技术允许从事件日志中提取信息。一般来说,过程挖掘有两个步骤:关联定义或发现,然后是过程推理或组合。首先,作者的工作包括从两个应用程序的日志轨迹中挖掘小模式;SKYPE和VIBER,这些模式是业务流程执行轨迹的表示。在这一步中,作者使用现有的技术;模式由有限状态自动机或它们的正则表达式表示;最后一个模型是由正则表达式ab*和ab*c*表示的两种类型的小模式的组合。其次,作者对这些模式进行并行计算,然后使用组合规则对这些小模式进行组合,它们有两方:一是矿,从执行轨迹中发现模式,二是对这些小模式进行组合。模式挖掘和组合由自动化现有技术来说明。执行跟踪是由SKYPE和VIBER中的用户影响的不同操作。结果是一般的和精确的。它最大限度地减少了执行时间和信息丢失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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