@inproceedings{BelRigLam09-RICERCA-RCRA-IW,
author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
title = {Mining Probabilistic Declarative Process Models},
booktitle = { Session {R.i.C.e.R.c.A}: RCRA Incontri E Confronti of the 16th RCRA International Workshop on Experimental evaluation of algorithms for solving
problems with combinatorial explosion ({RCRA} 2009)
Reggio Emilia, Italy, 11-12 December 2009},
editor = {Marco Gavanelli and Toni Mancini},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRigLam09-RICERCA-RCRA-IW.pdf},
year = {2009},
keywords = {Process Mining, Learning from Interpretations, Business Processes, Probabilistic Relational Languages},
abstract = {The management of business processes has recently received a lot of attention from companies, since it can support efficiency improvement. We present an approach for mining process models that first induces a model in the SCIFF logical language and then translates the model into Markov logic, a language belonging to the field of statistical relational learning.
Markov logic attaches weights to first-order contraints, in order to obtain a final probabilistic classification of process traces better than the purely logical one. The data used for learning and testing belong to a real database of university students' careers.}
}
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