As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing "stranger" behaviours according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is "optimal" according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results as regards both the performance and the quality of the obtained solution.
翻译:过去几年来,随着理解和将业务流程正规化为模式的需要不断增长,过程发现研究领域已变得越来越重要,发展了两种不同类型的模式代表方法:程序和宣示性。对于这一分类,绝大多数作品认为,发现任务是一个以记录在输入日志中的痕迹为指导的单级监督学习过程。在这项工作中,我们侧重于宣示性进程,将程序发现这一不太受欢迎的观点作为二进制监督学习任务,输入日志既报告正常系统执行的实例,又报告根据域语义代表“紧张”行为的痕迹。因此,我们深化了如何将这两组人提供的宝贵信息提取并正规化成一种模式,按照用户定义的目标,“最佳”的。我们的方法,即NegDis,是评估该领域其他相关作品的w.r.t.,并显示在获得的解决方案的性能和质量方面有希望的结果。