Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure
Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure
复制标题
结构化流程模型的自动发现：发现结构化与发现和结构
DOI：
10.1007/978-3-319-46397-1_25
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Bruno
中科院分区：
文献类型：
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作者：
Adriano Augusto;R. Conforti;M. Dumas;M. Rosa;G. Bruno
本文讨论了从事件日志中发现业务流程模型的问题。解决这个问题的现有方法在所发现模型的准确性和可理解性之间进行了各种权衡。关于第二个标准，经验研究表明，块结构的流程模型通常比非结构化的流程模型更容易理解，也更不容易出错。因此，几种自动化过程发现方法通过构造来生成块结构模型。然而，这些方法将制作准确模型的关注与确保其结构性的关注相混淆，有时牺牲前者以确保后者。在本文中，我们提出了一种替代方法，分离这两个问题。我们不是直接发现结构化的流程模型，而是首先应用一种众所周知的启发式方法，发现更准确但有时是非结构化的（甚至是不合理的）流程模型，然后将结果模型转换为结构化模型。实验评估表明，我们的“发现和结构”的方法优于传统的“发现结构化”的方法，在一系列的准确性和复杂性的措施。
This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.