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Discovering Instance-Spanning Constraints from Process Execution Logs Based on Classification Techniques

机译：基于分类技术从流程执行日志中发现跨实例约束

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Process-aware Information Systems (PAIS) have become ubiquitous in companies. Thus the amount of data that can be used to analyze and monitor process executions is vast. The event logs generated by PAIS might contain information about decision making processes and can support the understanding and improving of procedures in companies. Mining decisions and constraints from logs has already been investigated, but so far only for each instance in a separate manner. However, in many practical settings instances are connected to each other if they share, for example, the same resources. Therefore, we present an approach for discovering Instance-Spanning Constraints (ISC) from event logs. The main idea is to identify instance-spanning attributes in the logs and to separate the logs accordingly. Based on these projections, classification algorithms are applied in order to obtain ISC candidates. The feasibility and applicability of the approach is evaluated based on artificial as well as real-life logs. The discovered ISC candidates are then assessed by domain experts.
机译：流程感知信息系统（PAIS）在公司中已无处不在。因此，可用于分析和监视流程执行的数据量很大。由PAIS生成的事件日志可能包含有关决策过程的信息，并且可以支持对公司程序的理解和改进。已经研究了从日志中挖掘决策和约束的方法，但是到目前为止，仅针对每个实例以单独的方式进行了研究。但是，在许多实际设置中，如果实例共享例如相同的资源，则它们彼此连接。因此，我们提出了一种从事件日志中发现实例跨度约束（ISC）的方法。主要思想是在日志中标识跨实例的属性，并相应地分隔日志。基于这些预测，应用分类算法以获得ISC候选者。该方法的可行性和适用性基于人工和真实日志进行评估。然后由领域专家评估发现的ISC候选者。

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