Mining process models with non-free-choice constructs

By Lijie Wen, Wil M.P. van der Aalst, Jianmin Wang, Jiaguang Sun. Data mining and knowledge discovery, 2007

341 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/3cf0b2fa44c38feb0276d22e93f2719889b0667a

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In Computational Theory and Mathematics

In Information Systems

In Management Information Systems

Citations in the corpus

Discovering Block-Structured Process Models from Event Logs - A Constructive Approach (2013, background): “Techniques that discover process models from ordering relations of activities, such as the α algorithm [5] and its derivatives [25,26], guarantee isomorphic-rediscoverability for rather small classes of models [6] and do not guarantee fitness or soundness.”
On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery (2012, background): “A class of process discovery algorithms that strongly focusses on simplicity is the class of α-algorithms [3, 11, 20], derived from the original α algorithm [5].”
Automated Discovery of Process Models from Event Logs: Review and Benchmark (2017, background): “le), cf. Table 1. 3 IN4 The study is peer-reviewed. This criterion guarantees a minimum reasonable quality of the studies included in this SLR. IN5 The study is written in English. , +, ++ [9], [10], [11] $ [12] AGNEs Miner [13] — (DT) Genetic Miner [14], [15] Evolutionary Tree Miner [16] Heuristics”
Process Discovery using Integer Linear Programming (2009, neutral): “This problem naturally arises for models derived by efficient but imprecise mining algorithms such as the α miner [7] and some variants thereof [24, 29].”
Process mining: a two-step approach to balance between underfitting and overfitting (2008, methodology): “For example, the α++ algorithm presented in [41] and implemented in ProM can discover the non-local dependencies between D (E) and K (L).”