Conformance Checking with Regulations

法规符合性检查

• 批准号: 465904964
• 负责人: Professorin Dr. Luise Pufahl
• 金额: --
• 依托单位: Fachgebiet Software and Business Engineering (SBE)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/465904964?language=en
• 关键词: Conformance; Checking; Regulations

Business processes often have to follow specific prescribed regulations, such as clinical practice guidelines in healthcare, laws and statutes in public administration, or the new hygiene rules in many different domains. For both organizational success and official audits, it is essential to know: Are we following the prescribed regulations? If we deviate, why? Should we improve employees’ training? Could the rules be adapted to be better applicable in the real world?A major benefit of process mining is that it provides insights into the real execution of business processes and techniques for evidence-based process analysis. Conformance checking, a main task of process mining, comprises techniques for checking the relation between a designed process model and the real-life behavior, and identify as well as analyze deviation between them. Hence, it allows organizations to answer the above-raised questions. However, to realize conformance checking on process regulations, major challenges need to be addressed. First, regulations are typically written as long and ambiguous texts, so they cannot be used directly for conformance checking. Reference models, i.e., process model templates that serve to be re-used for the design of other processes, can bridge this gap between regulations and process execution data. However, transforming regulations into reference models requires high manual effort, and research on automating this step is still at the beginning. Second, modern conformance checking techniques are computationally efficient, but unable to judge the deviation’s relevance, consider multiple models, or decipher desirable (positive) from undesirable (negative) deviations, all of which are important when dealing with regulations. In addition, there is only limited research on how to visualize conformance checking results, particularly if the intended audience is business users. Finally, process execution data is much more detailed than reference models, such that a high degree of automated abstraction is required to connect them.The CheR project combines, for the first time, techniques from reference modeling and conformance checking to compare real-life process behavior with prescribed regulations. The goal is to find and visualize the deviations between them to allow tailored training for employees, preparation of audits, or suggestions for improving either the process or the regulations in the respective domains. Several open aspects need to be targeted to allow conformance checking with reference models, including (1) supporting (semi)-automatic generation of reference models, (2) the extraction of useful event logs for this type of process mining project, (3) benchmarking of existing conformance checking methods and their possible extension, and (4) an empirical evaluation on how the CheR approach allows to leverage conformance checking with regulations, e.g., for training employees.

业务流程通常必须遵循特定的规定，例如医疗保健中的临床实践指南，公共管理中的法律和法规，或许多不同领域的新卫生规则。对于组织的成功和官方审计来说，必须知道：我们是否遵循了规定的规则？如果我们偏离，为什么？我们应该改善员工的培训吗？这些规则是否可以修改，以便更好地适用于真实的世界？流程挖掘的一个主要好处是，它提供了对业务流程的真实的执行的深入了解，以及基于证据的流程分析技术。一致性检查是流程挖掘的一项主要任务，它包括检查设计的流程模型与实际行为之间的关系，识别和分析它们之间的偏差。因此，它允许各组织回答上述问题。然而，要实现对过程法规的一致性检查，需要解决主要挑战。首先，法规通常被写成冗长且模糊的文本，因此它们不能直接用于合规性检查。参考模型，即，用于被重新用于其它过程的设计的过程模型模板可以在规则和过程执行数据之间架起桥梁。然而，将法规转换为参考模型需要大量的人工工作，而自动化这一步骤的研究仍处于起步阶段。其次，现代一致性检查技术在计算上是高效的，但无法判断偏差的相关性，考虑多个模型，或从不期望的（负面的）偏差中破译出期望的（正面的）偏差，所有这些在处理法规时都很重要。此外，关于如何可视化一致性检查结果的研究非常有限，特别是如果目标受众是业务用户。最后，流程执行数据比参考模型详细得多，因此需要高度的自动化抽象来连接它们。CheR项目首次结合了参考建模和一致性检查的技术，以将实际流程行为与规定的法规进行比较。目标是找到并可视化它们之间的偏差，以便为员工提供量身定制的培训，准备审计，或提出改进相应领域的流程或法规的建议。需要针对几个开放方面来允许与参考模型的一致性检查，包括（1）支持参考模型的（半）自动生成，（2）为这种类型的流程挖掘项目提取有用的事件日志，（3）现有一致性检查方法的基准测试及其可能的扩展，以及（4）关于CheR方法如何允许利用与法规的一致性检查的经验评估，例如，用于培训员工。