通过 N 符索引从跟踪前缀高效在线计算业务流程状态

David Chapela-Campa, Marlon Dumas
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引用次数: 0

摘要

本文探讨了以下问题:给定一个流程模型和一个包含流程中正在发生的案例的跟踪前缀的事件日志,将每个案例映射到模型中的相应状态(即标记)。这种状态计算操作是日志动画和短期模拟等其他流程挖掘操作的基石。解决状态计算问题的一种方法是对模型中的每个跟踪前缀执行基于标记的重放。然而,当跟踪前缀并不严格遵循流程模型的行为时,标记重放可能会产生一个无法从流程初始状态到达的状态。另一种方法是首先计算每个正在进行的案例的跟踪前缀与模型之间的对齐度,然后重放对齐的跟踪前缀。然而,(前缀)对齐的计算成本很高。本文提出了一种方法,即在给定一个正在处理的案例的跟踪前缀的情况下,使用表示状态为 n-grams 的索引在恒定时间内计算其状态。实证评估表明,所提出的方法具有与前缀对齐方法相当的准确性,同时实现了每秒数十万条轨迹的吞吐量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing
This paper addresses the following problem: Given a process model and an event log containing trace prefixes of ongoing cases of a process, map each case to its corresponding state (i.e., marking) in the model. This state computation operation is a building block of other process mining operations, such as log animation and short-term simulation. An approach to this state computation problem is to perform a token-based replay of each trace prefix against the model. However, when a trace prefix does not strictly follow the behavior of the process model, token replay may produce a state that is not reachable from the initial state of the process. An alternative approach is to first compute an alignment between the trace prefix of each ongoing case and the model, and then replay the aligned trace prefix. However, (prefix-)alignment is computationally expensive. This paper proposes a method that, given a trace prefix of an ongoing case, computes its state in constant time using an index that represents states as n-grams. An empirical evaluation shows that the proposed approach has an accuracy comparable to that of the prefix-alignment approach, while achieving a throughput of hundreds of thousands of traces per second.
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