Process Discovery Contest @ BPM [1st Edition]

The Process Discovery approach described in the submitted document is directed towards discovery of process models from a Training Event log representing 10 different real time business process executions, and cross-validating the derived model with a set of two Test Event logs provided for evaluation of the process discovery technique. Each of the Test event logs ((test_log_april_1 to test_log_april_10) and (test_log_may_1 to test_log_may_10)) represents part of the model from the Training Log with complete total of 20 traces for each of the logs, and are characterized by having 10 traces that can be replayed (allowed) and 10 traces that cannot be replayed (disallowed) by the model. The total number of traces for the Test event logs (i.e. April log and May log) is therefore ((10 logs x 20 traces) x 2) = 400 Traces. Our aim is to carry out a classification task to determine the 400 individual traces that makes up the two test event log and then provide a Petri Net representation of the Training model as well as Business Process Model Notation (BPMN) mapping that allows for testing and evaluation of the behaviours/traces recorded in the Test logs. The objective of the proposed approach is to discover and provide process models that matches the original process models in term of balancing between “overfitting” and “underfitting”. A process model is seen as overfitting (the event log) if it is too restrictive, disallowing behaviour which is part of the underlying process. On the other hand, it is underfitting (the reality) if it is not restrictive enough, allowing behaviour which is not part of the underlying process. Following this challenge, we aim to provide a model which is as good in balancing “overfitting” and “underfitting” as it is able to correctly classify the traces that can be replayed in the “test” event log:
Thus,
• Given a trace (t) representing real process behaviour, the process model (m) classifies it as allowed, or
• Given a trace (t) representing a behaviour not related to the process, the process model (m) classifies it as disallowed.
The submitted document contains the classification attempts for the events logs provided and discusses the replaying semantics of the process modelling notation that has been employed. In other words, we discuss how, given any process trace t (for the Test event Log) and process model m (for the training log) in the discovered Petri Net and BPMN replaying notation, it can be unambiguously determined whether or not trace t can be replayed on model (m). We also provide a description of the tools used to discover the process models as well as checking the result of the classification task.

所述文档中描述的过程发现方法旨在从代表10个不同实时业务流程执行的训练事件日志中挖掘过程模型，并通过一套用于评估过程发现技术的两个测试事件日志对所导出的模型进行交叉验证。每个测试事件日志（包括从test_log_april_1到test_log_april_10以及从test_log_may_1到test_log_may_10）代表了训练日志中模型的一部分，且每个日志包含完整的20个轨迹。这些轨迹具有10个可重放（允许）和10个不可重放（不允许）的特征。因此，测试事件日志的总轨迹数为（10个日志 x 20个轨迹）x 2 = 400个轨迹。我们的目标是执行一个分类任务，以确定构成两个测试事件日志的400个单独轨迹，并提供训练模型的Petri网表示以及业务流程模型符号（BPMN）映射，以便对测试日志中记录的行为/轨迹进行测试和评估。所提出方法的目标是发现并提供与原始过程模型在平衡“过拟合”与“欠拟合”方面相匹配的过程模型。如果一个过程模型（事件日志）过于严格，不允许属于底层流程的行为，则被视为过拟合。相反，如果一个模型不足以限制，允许不属于底层流程的行为，则被视为欠拟合。面对这一挑战，我们旨在提供一个模型，其平衡“过拟合”与“欠拟合”的能力与它正确分类“测试”事件日志中可重放轨迹的能力相当：因此，• 对于代表真实过程行为的轨迹（t），过程模型（m）将其分类为允许；或• 对于代表与流程无关的行为的轨迹（t），过程模型（m）将其分类为不允许。所提交的文档包含了为提供的事件日志进行的分类尝试，并讨论了所采用的过程建模符号的重放语义。换句话说，我们讨论了在给定的任何过程轨迹t（针对测试事件日志）和过程模型m（针对训练日志）在发现的Petri网和BPMN重放符号中，如何明确地确定轨迹t是否可以在模型（m）上重放。我们还提供了用于发现过程模型以及检查分类任务结果的工具描述。