A universal significant reference model set for process mining evaluation framework

Lecture Notes in Business Information Processing

Volumn 181 LNBIP, Issue , 2014, Pages 16-30

  Guo, Qinlong ^a   Wen, Lijie ^a   Wang, Jianmin ^a   Ding, Zizhe ^b   Lv, Cheng ^a  

^a TSINGHUA UNIVERSITY   (China)

^b CHINA MOBILE COMMUNICATIONS CORPORATION   (China)

Author keywords

Feature Selection; Process Mining; Universal Significant Reference Model

Indexed keywords

ENTERPRISE RESOURCE MANAGEMENT; FEATURE EXTRACTION; REGRESSION ANALYSIS;

BUSINESS PROCESS MODEL; EVALUATION FRAMEWORK; PROCESS MINING; PROCESS MODELING; REAL LIFE DATASETS; REFERENCE MODELING; REFERENCE MODELS; REGRESSION MODEL;

DATA MINING;

EID: 84904537794     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-08222-6_2     Document Type: Conference Paper

References (17)

1. Wang, J., Wong, R. K., Ding, J., Guo, Q., Wen, L.: Efficient Selection of Process Mining Algorithms. IEEE Transactions on Services Computing 6(4), 484-496 (2013), doi: 10. 1109/TSC. 2012. 20.
2. Wang, J., Jin, T., Wong, R. K., Wen, L.: Querying business process model repositories-A survey of current approaches and issues. WorldWideWeb 17(3), 427-454 (2014).
3. Wang, J., Tan, S., Wen, L., Wong, R. K., Guo, Q.: An empirical evaluation of process mining algorithms based on structural and behavioral similarities. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 211-213. ACM (2012).
4. Wang, J., Wong, R. K., Ding, J., Guo, Q., Wen, L.: On recommendation of process mining algorithms. In: IEEE 19th International Conference on Web Services (ICWS), pp. 311-318. IEEE (2012).
5. Mendling, J.: Metrics for process models. LNBIP, vol. 6. Springer, Heidelberg (2008).
6. de Medeiros, A. A., Gnther, C. W., Weijters, A. J. M. M., van der Aalst, W. M.: Towards an evaluation framework for process mining algorithms. Beta, Research School for Operations Management and Logistics (2007).
7. Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128-1142 (2004).
8. Van der Aalst, W. M., Weijters, A. J. M. M.: Process mining: A research agenda. Computers in Industry 53(3), 231-244 (2004).
9. Jin, T., Wang, J., Wu, N., La Rosa, M., ter Hofstede, A. H. M.: Efficient and accurate retrieval of business process models through indexing. In: Meersman, R., Dillon, T. S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 402-409. Springer, Heidelberg (2010).
10. Wang, J., He, T., Wen, L., Wu, N., ter Hofstede, A. H. M., Su, J.: A behavioral similarity measure between labeled Petri nets based on principal transition sequences. In: Meersman, R., Dillon, T. S., Herrero, P. (eds.) OTM 2010. LNCS, vol. 6426, pp. 394-401. Springer, Heidelberg (2010).
11. Wen, L., van der Aalst, W. M., Wang, J., Sun, J.: Mining process models with nonfree-choice constructs. DataMining andKnowledge Discovery 15(2), 145-180 (2007).
12. Wen, L., Wang, J., van der Aalst, W. M., Huang, B., Sun, J.: Mining process models with prime invisible tasks. Data & Knowledge Engineering 69(10), 999-1021 (2010).
13. de Medeiros, A. K. A., Weijters, A. J., van der Aalst, W. M.: Genetic process mining: An experimental evaluation. Data Mining and Knowledge Discovery 14(2), 245-304 (2007).
14. Weijters, A. J. M. M., van der Aalst, W. M., De Medeiros, A. A.: Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven, Tech. Rep. WP, 166 (2006).
15. Van der Aalst, W. M., Rubin, V., van Dongen, B. F., Kindler, E., Gnther, C. W.: Process mining: A two-step approach using transition systems and regions. BPM Center Report BPM-06-30, BPMcenter. org. (2006).
16. Dijkman, R., Dumas, M., García-Bañuelos, L.: Graph matching algorithms for business process model similarity search. In: Dayal, U., Eder, J., Koehler, J., Reijers, H. A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 48-63. Springer, Heidelberg (2009).
17. Zha, H., Wang, J., Wen, L., Wang, C., Sun, J.: A workflow net similarity measure based on transition adjacency relations. Computers in Industry 61(5), 463-471 (2010).