Context-sensitive textual recommendations for incomplete process model elements

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9253, Issue , 2015, Pages 189-197

  Pittke, Fabian ^a   Richetti, Pedro H Piccoli ^b   Mendling, Jan ^a   Baião, Fernanda Araujo ^b  

^a VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS   (Austria)

^b FEDERAL UNIVERSITY OF THE STATE OF RIO DE JANEIRO   (Brazil)

Author keywords

Business process models; Context sensitive recommendations; Incompleteness of model elements

Indexed keywords

ENTERPRISE RESOURCE MANAGEMENT;

BUSINESS PROCESS MODEL; CONTEXT SENSITIVE; MODEL ELEMENTS; PROCESS ELEMENTS; PROCESS MODELING; REAL-WORLD PROCESS; STRUCTURAL LEVELS; USER EXPERIMENTS;

ADMINISTRATIVE DATA PROCESSING;

EID: 84944698745     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-23063-4_13     Document Type: Conference Paper

References (27)

1. Sinur, J., Hill, J.B.: Magic quadrant for business process management suites. Gartner RAS Core Research Note, 1-24 (2010)
2. Krogstie, J., Sindre, G.: Jørgensen, H.: Process models representing knowledge for action: a revised quality framework. Eur. J. Inf. Syst. 15(1), 91-102 (2006)
3. Davenport, T.H., Short, J.E.: The new industrial engineering: Information technology and business process redesign. Sloan Mgmt. Review 31(4), 11-27 (1990)
4. Dumas, M., Van der Aalst, W.M., Ter Hofstede, A.H.: Process-aware information systems: bridging people and software through process technology. Wiley (2005)
5. Mendling, J.: Empirical Studies in Process Model Verification. In: Jensen, K., van der Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 208-224. Springer, Heidelberg (2009)
6. Mendling, J., Leopold, H., Pittke, F.: 25 challenges of semantic process modeling. Int. Journal of Inf. Systems and Software Eng. for Big Companies 1(1), 78-94 (2015)
7. Kluza, K., Baran, M., Bobek, S., Nalepa, G.J.: Overview of recommendation techniques in business process modeling. Knowledge Eng. and Software Eng., 46-57 (2013)
8. Clever, N., Holler, J., Shitkova, M., Becker, J.: Towards auto-suggested process modeling-prototypical development of an auto-suggest component for process modeling tools. In: EMISA, pp. 133-145 (2013)
9. Koschmider, A., Oberweis, A.: Recommendation-based business processes design. In: Handbook on Business Process Management 1. Springer, pp. 323-336 (2015)
10. Rosemann, M.: Potential Pitfalls of Process Modeling: Part A. Business Process Management Journal 12(2), 249-254 (2006)
11. Leopold, H., Eid-Sabbagh, R.H., Mendling, J., Azevedo, L.G., Baião, F.A.: Detection of naming convention violations in process models for different languages. Decision Support Systems 56, 310-325 (2013)
12. Leopold, H., Mendling, J., Reijers, H.A., La Rosa, M.: Simplifying process model abstraction: Techniques for generating model names. Inf. Sys. 39, 134-151 (2014)
13. Keller, G., Teufel, T.: SAP(R) R/3 Process Oriented Implementation: Iterative Process Prototyping. Addison-Wesley (1998)
14. Mendling, J., Reijers, H.A., van der Aalst, W.M.P.: Seven Process Modeling Guidelines (7PMG). Information and Software Technology 52(2), 127-136 (2010)
15. Mendling, J., Reijers, H.A., Recker, J.: Activity Labeling in Process Modeling: Empirical Insights and Recommendations. Inf. Sys. 35(4), 467-482 (2010)
16. Gordijn, J., Akkermans, H., van Vliet, H.: Business modelling is not process modelling. In: Mayr, H.C., Liddle, S.W., Thalheim, B. (eds.) ER Workshops 2000. LNCS, vol. 1921, pp. 40-51. Springer, Heidelberg (2000)
17. Barjis, J.: The importance of business process modeling in software systems design. Science of Computer Programming 71(1), 73-87 (2008)
18. del Olmo, F.H., Gaudioso, E.: Evaluation of recommender systems: A new approach. Expert Systems with Applications 35(3), 790-804 (2008)
19. Richetti, P.H.P., Baião, F.A., Santoro, F.M.: Declarative process mining: reducing discovered models complexity by pre-processing event logs. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 400-407. Springer, Heidelberg (2014)
20. Bracewell, D.B., Russell, S., Wu, A.S.: Identification, expansion, and disambiguation of acronyms in biomedical texts. In: Chen, G., Pan, Y., Guo, M., Lu, J. (eds.) ISPA-WS 2005. LNCS, vol. 3759, pp. 186-195. Springer, Heidelberg (2005)
21. Lin, D.: An information-theoretic definition of similarity. ICML 98, 296-304 (1998)
22. Piegorsch, W.W.: Sample sizes for improved binomial confidence intervals. Computational Statistics & Data Analysis 46(2), 309-316 (2004)
23. Hornung, T., Koschmider, A., Oberweis, A.: A recommender system for business process models. In: Workshop on Information Technologies & Systems (2009)
24. Hornung, T., Koschmider, A., Oberweis, A.: Rule-based autocompletion of business process models. In: CAiSE Forum, vol. 247 (2007)
25. Markovic, I., Pereira, A.C.: Towards a formal framework for reuse in business process modeling. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 484-495. Springer, Heidelberg (2008)
26. Hornung, T., Koschmider, A., Lausen, G.: Recommendation based process modeling support: method and user experience. In: Li, Q., Spaccapietra, S., Yu, E., Olivé, A. (eds.) ER 2008. LNCS, vol. 5231, pp. 265-278. Springer, Heidelberg (2008)
27. Bobek, S., Baran, M., Kluza, K., Nalepa, G.J.: Application of bayesian networks to recommendations in business process modeling. In: AIBP@ AI* IA, pp. 41-50 (2013)