@inproceedings{BelRigLam10-KSEM10-IC,
author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
title = {Probabilistic Declarative Process Mining},
booktitle = {Proceedings of the 4th International Conference on Knowledge Science, Engineering \& Management ({KSEM 2010}),
Belfast, UK, September 1-3, 2010},
year = {2010},
editor = {Bi, Yaxin and Williams, Mary-Anne},
abstract = {
The management of business processes is receiving much attention, since it can
support signicant eciency improvements in organizations. One of the most
interesting problems is the representation of process models in a language that
allows to perform reasoning on it. Various knowledge-based languages have been
lately developed for such a task and showed to have a high potential due to the
advantages of these languages with respect to traditional graph-based notations.
In this work we present an approach for the automatic discovery of knolwedge-
based process models expressed by means of a probabilistic logic, starting from
a set of process execution traces. The approach first uses the DPML (Declarative
Process Model Learner) algorithm to extract a set of integrity constraints from
a collection of traces. Then, the learned constraints are translated into Markov
Logic formulas and the weights of each formula are tuned using the Alchemy
system. The resulting theory allows to perform probabilistic classication of
traces. We tested the proposed approach on a real database of university
students' careers. The experiments show that the combination of DPML and Alchemy
achieves better results than DPML alone.},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
address = {Heidelberg, \Germany},
volume = {6291},
pages = {292--303},
doi = {10.1007/978-3-642-15280-1_28},
pdf = {http://www.springerlink.com/content/h85k601v74850h5p/},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRIgLam-KSEM10.pdf},
copyright = {Springer},
note = {The original publication is available at \url{http://www.springerlink.com}}
}
@inproceedings{BelRigLam10-CILC10-NC,
author = {Elena Bellodi and Fabrizio Riguzzi and Evelina Lamma},
title = {Probabilistic Logic-based Process Mining},
booktitle = {Proceedings of the 25th Italian Conference on Computational Logic ({CILC2010}),
Rende, Italy, July 7-9, 2010.},
year = {2010},
abstract = {
The management of business processes has recently received much attention, since it can support significant efficiency improvements in organizations. One of the most interesting problems is the description of a process model in a language, also equipped with an operational support, that allows checking the compliance of a process execution (trace) to the model. Another problem of interest is the induction of these models from data.
In this paper, we present a logic-based approach for the induction of process models that are expressed by means of a probabilistic logic.
The approach first uses the DPML algorithm to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights for each formula are tuned using the Alchemy system.
The resulting theory allows to perform probabilistic classification of traces.
We tested the proposed approach on a real database of university students' careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
address = {Aachen, \Germany},
volume = {598},
pdf = {http://ceur-ws.org/Vol-598/paper17.pdf},
url = {http://ml.unife.it/wp-content/uploads/Papers/BelRigLam-CILC10.pdf},
copyright = {by the authors}
}
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