论文标题
使用增量状态空间扩展的在线过程监视：精确算法
Online Process Monitoring Using Incremental State-Space Expansion: An Exact Algorithm
论文作者
论文摘要
（业务）流程的执行会在公司内部使用的信息系统中生成有价值的事件数据痕迹。最近，在过程挖掘领域（即在线符合度检查）中开发了监视执行流程执行正确性的方法。监视过程“执行过程中的合规性”的优点是明确的，即，一旦发生，就会立即检测到偏差，并立即开始对策，以减少过程偏差造成的负面影响。在线一致性检查中的现有工作仅允许获得不符合性的近似值，例如高估了偏差的实际严重性。在本文中，我们提出了一种精确的，无参数的在线一致性检查算法，该算法可以即时计算一致性检查结果。我们的算法利用了一个事实，即通过逐步扩展搜索空间并重用先前计算的中间结果，可以将符合性检查问题减少到最短路径问题。我们的实验表明，我们的算法优于可比的最新近似算法。
The execution of (business) processes generates valuable traces of event data in the information systems employed within companies. Recently, approaches for monitoring the correctness of the execution of running processes have been developed in the area of process mining, i.e., online conformance checking. The advantages of monitoring a process' conformity during its execution are clear, i.e., deviations are detected as soon as they occur and countermeasures can immediately be initiated to reduce the possible negative effects caused by process deviations. Existing work in online conformance checking only allows for obtaining approximations of non-conformity, e.g., overestimating the actual severity of the deviation. In this paper, we present an exact, parameter-free, online conformance checking algorithm that computes conformance checking results on the fly. Our algorithm exploits the fact that the conformance checking problem can be reduced to a shortest path problem, by incrementally expanding the search space and reusing previously computed intermediate results. Our experiments show that our algorithm outperforms comparable state-of-the-art approximation algorithms.
