Discovering context-aware models for predicting business process performances

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

Volumn 7565 LNCS, Issue PART 1, 2012, Pages 287-304

  Folino, Francesco ^a   Guarascio, Massimo ^a   Pontieri, Luigi ^a  

^a ICAR CNR   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BUSINESS PROCESS; CLUSTERING APPROACH; CONTEXT-AWARE; EMERGING TOPICS; EXECUTION SCENARIO; FLEXIBLE BUSINESS PROCESS; IN-PROCESS; PERFORMANCE MEASURE; PERFORMANCE PATTERNS; PERFORMANCE PREDICTOR; PREDICTIVE MODELS; PROCESSING TIME; RUNNING PROCESS; RUNTIMES; SEASONALITY; SYSTEM PROTOTYPE;

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FORECASTING;

EID: 84872817639     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33606-5_18     Document Type: Conference Paper

References (14)

1. CLUS: A predictive clustering system, http://dtai.cs.kuleuven.be/clus/
2. Blockeel, H., Raedt, L.D.: Top-down induction of first-order logical decision trees. Artificial Intelligence 101(1-2), 285-297 (1998)
3. Blockeel, H., Raedt, L.D., Ramon, J.: Top-down induction of clustering trees. In: Proc. of 15th Intl. Conference on Machine Learning (ICML1998). pp. 55-63 (1998)
4. Caragea, C., Silvescu, A., Caragea, D., Honavar, V.: Abstraction augmented Markov models. In: Proc. of 2010 IEEE Int. Conf. on Data Mining (ICDM 2010), pp. 68-77 (2010)
5. Conforti, R., Fortino, G., La Rosa, M., ter Hofstede, A.H.M.: History-Aware, Real- Time Risk Detection in Business Processes. In: Meersman, R., Dillon, T., Herrero, P., Kumar, A., Reichert, M., Qing, L., Ooi, B.-C., Damiani, E., Schmidt, D.C., White, J., Hauswirth, M., Hitzler, P., Mohania, M. (eds.) OTM 2011, Part I. LNCS, vol. 7044, pp. 100-118. Springer, Heidelberg (2011)
6. van Dongen, B.F., Crooy, R.A., van der Aalst, W.M.P.: Cycle Time Prediction: When Will This Case Finally Be Finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part I. LNCS, vol. 5331, pp. 319-336. Springer, Heidelberg (2008)
7. Folino, F., Greco, G., Guzzo, A., Pontieri, L.: Mining usage scenarios in business processes: Outlier-aware discovery and run-time prediction. Data & Knowledge Engineering 70(12), 1005-1029 (2011)
8. Greco, G., Guzzo, A., Pontieri, L., Saccà, D.: Discovering expressive process models by clustering log traces. IEEE Trans. on Knowl. and Data Engineering 18(8), 1010-1027 (2006)
9. Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P.: Supporting Flexible Processes through Recommendations Based on History. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51-66. Springer, Heidelberg (2008)
10. Song, M., Günther, C.W., van der Aalst, W.M.P.: Trace Clustering in Process Mining. In: Ardagna, D., Mecella, M., Yang, J. (eds.) BPM 2008 Workshops. LNBIP, vol. 17, pp. 109-120. Springer, Heidelberg (2009)
11. van der Aalst, W.M.P., van Dongen, B.F., Herbst, J., Maruster, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: a survey of issues and approaches. Data & Knowledge Engineering 47(2), 237-267 (2003)
12. van der Aalst,W.M.P., van Dongen, B.F., Günther, C.W., Mans, R.S., de Medeiros, A.K.A., Rozinat, A., Rubin, V., Song, M., Verbeek, H.M.W.,Weijters, A.J.M.M.T.: ProM 4.0: Comprehensive Support for Real Process Analysis. In: Kleijn, J., Yakovlev, A. (eds.) ICATPN 2007. LNCS, vol. 4546, pp. 484-494. Springer, Heidelberg (2007)
13. van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Information Systems 36(2), 450-475 (2011)
14. de la Vara, J.L., Ali, R., Dalpiaz, F., Sánchez, J., Giorgini, P.: COMPRO: A Methodological Approach for Business Process Contextualisation. In: Meersman, R., Dillon, T.S., Herrero, P. (eds.) OTM 2010, Part I. LNCS, vol. 6426, pp. 132-149.