Abstract
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 aforementioned 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.
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- 1.
In some cases, if the absence of token-generators is not guaranteed, we use \(c(a,t) = \epsilon \), where \(\epsilon \) is a positive real number smaller than 1 and close to 0.
- 2.
The label \([p_2, p'_2, p'_3]\) is not shown in Fig. 4, it corresponds to the state on the second row and second column.
- 3.
In case we solve an ILP, we enforce \(\vec {x} \in \mathbb {N}^{|T^{S}|}\).
- 4.
In practice, we cache h-values, thus we only derive a new h-value if we did not compute an exact h-value in an earlier stage.
- 5.
Both experiments ran on the same machine in this instance.
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van Zelst, S.J., Bolt, A., van Dongen, B.F. (2018). Computing Alignments of Event Data and Process Models. In: Koutny, M., Kristensen, L., Penczek, W. (eds) Transactions on Petri Nets and Other Models of Concurrency XIII. Lecture Notes in Computer Science(), vol 11090. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58381-4_1
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