Understanding Spaghetti Models with Sequence Clustering for ProM
Understanding Spaghetti Models with Sequence Clustering for ProM
复制标题
了解具有 ProM 序列聚类的意大利面条模型
DOI：
10.1007/978-3-642-12186-9_10
复制
发表时间：
2009
影响因子：
8.9
通讯作者：
D. R. Ferreira
中科院分区：
文献类型：
--
作者：
Gabriel M. Veiga;D. R. Ferreira
流程挖掘的目标是从事件日志中发现流程模型。然而，对于结构不好且具有许多不同行为的流程，现有的流程挖掘技术会生成非常复杂的模型，这些模型通常很难理解；这些模型称为意大利面条模型。试图理解这些模型的一种方法是将日志分成簇，以便分析简化的案例集。然而，现实世界日志中存在的噪声和自组织行为的数量仍然是一个问题，因为这种类型的行为干扰了集群，并使所生成的集群的模型复杂化，影响了模式的发现。在本文中，我们提出了一种旨在克服这些困难的方法，即只提取有用的数据并以可理解的方式呈现它。该解决方案已在可编程只读存储器中实现，分为两个阶段：预处理和序列聚类。我们在一个案例研究中说明了这种方法，在这个案例中，即使在存在非常多样化和令人困惑的行为的情况下，也可以识别行为模式。
The goal of process mining is to discover process models from event logs. However, for processes that are not well structured and have a lot of diverse behavior, existing process mining techniques generate highly complex models that are often difficult to understand; these are called spaghetti models. One way to try to understand these models is to divide the log into clusters in order to analyze reduced sets of cases. However, the amount of noise and ad-hoc behavior present in real-world logs still poses a problem, as this type of behavior interferes with the clustering and complicates the models of the generated clusters, affecting the discovery of patterns. In this paper we present an approach that aims at overcoming these difficulties by extracting only the useful data and presenting it in an understandable manner. The solution has been implemented in ProM and is divided in two stages: preprocessing and sequence clustering. We illustrate the approach in a case study where it becomes possible to identify behavioral patterns even in the presence of very diverse and confusing behavior.