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Computing Alignments of Event Data and Process Models

机译:计算事件数据和过程模型的对齐

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The aim of conformance checking is to assess whether a process model and event data, recorded in an event log, conform to each other. In recent years, alignments have proven extremely useful for calculating conformance statistics. Computing optimal alignments is equivalent to solving a shortest path problem on the state space of the synchronous product net of a process model and event data. State-of-the-art alignment based conformance checking implementations exploit the A*-algorithm, a heuristic search method for shortest path problems, and include a wide range of parameters that likely influence their performance. In previous work, we presented a preliminary and exploratory analysis of the effect of these parameters. This paper extends the afore-mentioned work by means of large-scale statistically-sound experiments that describe the effects and trends of these parameters for different populations of process models. Our results show that, indeed, there exist parameter configurations that have a significant positive impact on alignment computation efficiency.
机译:一致性检查的目的是评估在事件日志中记录的过程模型和事件数据是否相互符合。近年来,对准已经证明对计算一致性统计非常有用。计算最佳对齐等效于解决过程模型和事件数据的同步产品网络的状态空间上的最短路径问题。基于最先进的对齐的一致性检查实现利用A * -Algorithm,启发式搜索方法以实现最短路径问题,包括可能影响其性能的广泛参数。在以前的工作中,我们提出了对这些参数效果的初步和探索性分析。本文通过大规模的统计学实验扩展了上述工作,该实验描述了这些参数对不同群体的过程模型的影响和趋势。我们的结果表明,实际上,存在对对准计算效率具有显着积极影响的参数配置。

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