Asia Pacific Business Process Management: Second Asia by Chun Ouyang, Jae-Yoon Jung

By Chun Ouyang, Jae-Yoon Jung

This booklet constitutes the complaints of the second one Asia Pacific convention on company procedure administration held in Brisbane, QLD, Australia, in July 2014.

In all, 33 contributions from 12 international locations have been submitted. After every one submission used to be reviewed by means of at the very least 3 application Committee participants, 9 complete papers have been authorized for e-book during this quantity. those 9 papers hide a number of subject matters that may be categorised below 4 major learn focuses in BPM: approach mining, procedure modeling and repositories, technique version comparability, and approach analysis.

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Additional info for Asia Pacific Business Process Management: Second Asia Pacific Conference, AP-BPM 2014, Brisbane, QLD, Australia, July 3-4, 2014, Proceedings (Lecture Notes in Business Information Processing)

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Deriving the regression model), and 3. recommending (i,e. applying the regression model to recommend the best suitable algorithm). For URM, the time cost is only composed of recommending. This is because that the models in URM only needs to be mined and trained once globally. And when recommending, the training result of URM can be just used without paying extra time on mining and training once again. Therefore, compared with the ORM, the time cost of URM is much smaller as shown in Table 2. A Universal Significant Reference Model Set 29 Table 2.

OTM 2010. LNCS, vol. 6426, pp. 402–409. Springer, Heidelberg (2010) 10. : A behavioral similarity measure between labeled Petri nets based on principal transition sequences. , Herrero, P. ) OTM 2010. LNCS, vol. 6426, pp. 394–401. Springer, Heidelberg (2010) 11. : Mining process models with nonfree-choice constructs. Data Mining and Knowledge Discovery 15(2), 145–180 (2007) 12. : Mining process models with prime invisible tasks. Data & Knowledge Engineering 69(10), 999–1021 (2010) 13. : Genetic process mining: An experimental evaluation.

Figure 12(a), 12(b) and 12(c) show the speed result of Artificial, Boiler and Trains, respectively. Generally speaking, for the same model set and the same proportion training set, time cost of extracting 6 features is less than extracting 48 features. Besides, as the process models in Trains dataset are relatively complex, the time cost in Trains data set are more than other datasets. Meanwhile, the time cost of the Boiler dataset is least among these three datasets, because number of process models in Boiler is least.

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