文献：Merging Business process models (2010)

In one instance reported in this paper, it took a team of three analysts
130 man-hours to merge 25% of two variants of an end-to-end process model.

The merging method takes as input a collection of process models and generates a configurable process model. A configurable process model is a modeling artifact that captures a family of process models in an integrated manner and that allows analysts to understand what these process models share, what their differences are, and why and how these differences occur.

There exist many notations to represent business processes, such as Event-driven Process Chains (EPC), UML Activity Diagrams (UML ADs) and the Business Process Modeling Notation (BPMN). In this paper we abstract from any specific notation and represent a business process model as a directed graph with labeled nodes as per the following definition.

A configurable process model 是一个过程模型家族融合后的模型化制品，支持分析者理解这些过程模型共享的部分，不同的部分，以及这些差异为何发生以及如何发生。

the algorithm has been tested on process models sourced from different domains. The tests show that the process merging algorithm produces compact models and scales up to process models containing hundreds of nodes.

文献: Integrating computer log files for process mining: a Genetic algorithm inspired technique

主要工作：一组不同项目的事件日志文件，使用遗传算法把它们融合到一起

背景：过程发现（process discovery）业务流程被执行多次，日志中包含了一些同样流程的追踪。假设每个追踪包含以正确顺序注册的一个过程执行的不同事件。通过在这些轨迹中搜索重复模式，可以发现一般的过程流程。过程挖掘工具适用于单个日志文件。但一些业务流被不同软件工具支持，这些信息被记录在多个不同的日志文件中。

文中描述了一种基于GA的合并不同日志文件中不同traces的相同流程的技术。

$$\alpha=algorithm$$是一种过程发现的基础算法

Merge logs是按照时间戳合并相同事件

Convert log to Directly follows graph (Directly follows graph)

Mine for a Heuristics Net using Heuristics Mining (Mined Models)

相关工作研究背景

文献：Configurable process models（博士论文2009）

EPC在工业界有着广泛的应用，企业软件提供商在提供软件和相关文档的同时，提供参考模型，这些模型利用了咨询公司从各种以前的实施中得出的行业标准，即通过他们在特定领域的经验，给软件的使用者提供流程指导。 They not only rely on the offerings of software vendors, but also provide their own so-called reference models. These models utilize industrial standards which the consulting firms derive from various previous implementations, i.e., through their experience in the particular field.

过程模板和参考模型总是描述“最佳”的解决方案的一个业务流程。如果实际应用过程有偏差，需要手动调整过程模型。因此，即使很小的变化也需要过程建模的经验与技巧，并因此而产生风险。融合各种业务模型变体，将得到一个完整的过程模型，这个完整的模型将促使用户方便的选择所需模型。这种构建模型的方法能够保证模型的正确性，因为，构建完整模型的过程模型来自于真实的业务过程建模。

总结一下，可配置的过程模型可以重用各种现有的模型，通过组合它们并为模型用户提供针对或针对每个单独选择的选择来处理这些定义。 特别是当在参考过程建模和工作流程管理的BPM领域中使用时，它们的使用有望显着减少模型用户的手动过程建模工作量。

Summarizing, configurate process models enable reusing various existing process defination by combining them and providing the model user with a choice for or against each individual alternative. Especially when applied in the intersection of the BPM domains of reference process modeling and workflow management (see Figure 1.1) their use promises a signifi cant reduction of manual process modeling efforts for model users.

主要意义在于通过重用，减少重新建模的代价，重用的模型来自于已有的一些模型，通过选择（选择可用的，却）

EPC (event-driven process chains)

有三种类型的节点：function,event, connector

function: representing tasks that can be performed in an organization.

event: representing pre-conditions that must be satisfied before a function can be performed, or post-conditions that are satisfied after a function has been performed.

connector: which determine the flow of execution of the process.

EPCs have three kinds of connectors: AND, XOR and OR.

AND: 过程可以沿着多条并行路径继续（and-split），或者必须等待多个并行路径才能继续（and-join）

XOR:必须选择继续进行的路径（XOR-split），或者过程必须等待一个路径完成，以便允许继续（XOR-join）

OR:启动或等待多个路径。

EPC图融合原则：

  • 不同类型的节点不能融合：Nodes of different types must not be mapped
  • 分叉节点不能与合并节点融合：splits must not be matched with joins.
  • 根据匹配度来找到节点之间最好的映射The aim of matching two models is to establish the best mapping between their nodes.

映射是一个函数matching score：从第一个图的节点映射到第二个图的节点，主要思想是基于graph edit distance.

计算过程分为三步：

  1. 计算两个function或者event之间label的相似度。The matching score of a mapping between two functions or between two events is measured by the similarity of their labels. (组合句法相似度和语义相似度的方法)

  2. 根据上下文计算1步映射结果和connector节点的相似度。（context similarity）Given two mapped nodes, context similarity is the fraction of nodes in their transitive presets and their transitive postsets that are mapped (i.e. the contexts of the nodes), provided at least one mapping of transitive preset nodes and one mapping of transitive postset nodes exists.

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