@incollection{CatLamRigSto10-ISKM-BC,
author = {Massimiliano Cattafi and Evelina Lamma and Fabrizio Riguzzi and Sergio Storari},
title = {Incremental Declarative Process Mining},
booktitle = {Smart Information and Knowledge Management: Advances, Challenges,
and Critical Issues},
year = {2010},
editor = {Ngoc Thanh Nguyen and Edward Szczerbicki},
publisher = {Springer},
address = {Heidelberg, \Germany},
series = {Studies in Computational Intelligence},
issn = {1860-949X},
isbn = {978-3-642-04583-7},
doi = {10.1007/978-3-642-04584-4_5},
volume = {260},
pages = {103--127},
abstract = {Business organizations achieve their mission by performing a number
of processes. These span from simple sequences of actions to complex
structured sets of activities with complex interrelation among them. The
field of Business Processes Management studies how to describe, analyze,
preserve and improve processes. In particular the subfield of Process
Mining aims at inferring a model of the processes from logs (i.e. the
collected records of performed activities).
Moreover, processes can change over time to reflect mutated conditions, therefore
it is often necessary to update the model. We call this activity Incremental
Process Mining. To solve this problem, we modify the process mining system
DPML to obtain IPM (Incremental Process Miner), which employs a subset of the
SCIFF language to represent models and adopts techniques developed in
Inductive Logic Programming to perform theory revision. The experimental
results show that is more convenient to revise a theory rather than
learning a new one from scratch.
},
keywords = {Business Processes, Process Mining, Theory Revision},
url = {http://springerlink.com/content/663tx3001671j503/?p=1581a28611ac48088b750995a9767d5f&pi=4},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/CatLamRigSto09-ISKM-BC.pdf},
copyright = {Springer},
scopus = {2-s2.0-74049114164}
}
@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}
}
@article{MonTorAlb10-FI-IJ,
title = {{Abductive Logic Programming as an Effective Technology for the Static Verification of Declarative Business Processes}},
journal = {Fundamenta Informaticae},
year = {2010},
author = {Marco Montali and Paolo Torroni and Marco Alberti and Federico Chesani and Evelina Lamma and Paola Mello},
volume = {102},
number = {3-4},
pages = {325--361},
issn = {0169-2968},
note = {IF: 0.693},
doi = {10.3233/FI-2010-310},
abstract = {We discuss the static verification of declarative Business Processes. We identify four desiderata about verifiers, and propose a concrete framework which satisfies them. The framework is based on the ConDec graphical notation for modeling Business Processes, and on Abductive Logic Programming technology for verification of properties. Empirical evidence shows that our verification method seems to perform and scale better, in most cases, than other state of the art techniques (model checkers, in particular). A detailed study of our framework's theoretical properties proves that our approach is sound and complete when applied to ConDec models that do not contain loops, and it is guaranteed to terminate when applied to models that contain loops.}
}
@inproceedings{AlbGavLam10-ICLP-IC,
author = {Marco Alberti and Marco Gavanelli and Evelina Lamma},
title = {Runtime Addition of Integrity Constraints in Abductive Logic Programs },
booktitle = {Technical Communications of the 26th International Conference on Logic Programming},
year = 2010,
editor = {Manuel Hermenegildo and Torsten Schaub},
volume = 7,
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
address = {Dagstuhl, Germany},
month = {July},
pages = {4-13},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
url = {http://drops.dagstuhl.de/opus/volltexte/2010/2616},
doi = {10.4230/LIPIcs.ICLP.2010.4},
isbn = {978-3-939897-17-0},
issn = {1868-8969}
}
@inproceedings{AlbGavLam10-CILC-NW,
author = {Marco Alberti and Marco Gavanelli and Evelina Lamma},
title = {Runtime Addition of Integrity Constraints in {SCIFF}},
booktitle = {Proceedings of the 25th Italian Conference on Computational Logic},
year = {2010},
editor = {Wolfgang Faber and Nicola Leone},
volume = {598},
series = {CEUR workshop proceedings},
issn = {1613-0073},
url = {http://ceur-ws.org/Vol-598/paper02.pdf}
}
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