@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.}
}
@inproceedings{BraRig09-RICERCA-RCRA-IW,
author = {Stefano Bragaglia and Fabrizio Riguzzi},
title = {Approximate Inference for Logic Programs with Annotated Disjunctions},
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/BraRig09-RICERCA-RCRA-IW.pdf},
year = {2009},
keywords = {Probabilistic Reasoning, Probabilistic Logic Programming, Logic Programming, Logic Programs with Annotated Disjunctions},
abstract = {The paper presents two algoriothms for performing approximate inference on Logic Programs with Annotated Disjunctions: k-best and Monte Carlo. The first is based on branch and bound while the second is based on a stochastic approach.}
}
@article{Rig09-LJIGPL-IJ,
author = {Fabrizio Riguzzi},
title = {Extended Semantics and Inference for the {Independent Choice Logic}},
journal = {Logic Journal of the IGPL},
publisher = {Oxford University Press},
volume = {17},
number = {6},
pages = {589--629},
address = {Oxford, \UK},
year = {2009},
abstract = {The Independent Choice Logic (ICL) is a language for expressing
probabilistic information in logic programming that adopts a distribution
semantics: an ICL theory defines a distribution over a set of possible worlds
that are normal logic programs. The probability of a query is then given by the
sum of the probabilities of worlds where the query is true.
The ICL semantics requires the theories to be acyclic. This is a strong
limitation that rules out many interesting programs.
In this paper we present an extension of the ICL semantics that allows theories
to be modularly acyclic.
Inference with ICL can be performed with the Cilog2 system that computes
explanations to queries and then makes them mutually incompatible by means of
an iterative algorithm.
We propose the system PICL (for Probabilistic inference with ICL) that computes
the explanations to queries by means of a modification of SLDNF\--resolution
and then makes them mutually incompatible by means of Binary Decision Diagrams.
PICL and Cilog2 are compared on problems that involve computing the probability
of a connection between two nodes in biological graphs and social networks.
PICL turned to be more efficient, handling larger networks/more complex queries
in a shorter time than Cilog2. This is true both for marginal and for
conditional queries.
},
doi = {10.1093/jigpal/jzp025},
keywords = {Probabilistic Logic Programming, Independent Choice Logic, Modularly acyclic programs, SLDNF-Resolution},
copyright = {Fabrizio Riguzzi, exclusively licensed to Oxford University Press},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/Rig09-LJIGPL-IJ.pdf}
}
@inproceedings{Rig09-RCRA-IW,
author = {Fabrizio Riguzzi},
title = {The {SLGAD} Procedure for Inference on {Logic Programs with Annotated
Disjunctions}},
booktitle = {Proceedings of the 15th {RCRA} workshop on Experimental Evaluation
of Algorithms for Solving Problems with Combinatorial Explosion
Udine, Italy, December 12-13, 2008},
editor = {Marco Gavanelli and Toni Mancini},
url = {http://ceur-ws.org/Vol-451/paper15riguzzi.pdf},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
volume = {451},
year = {2009},
address = {Aachen, \Germany},
copyright = {by the authors}
}
@article{CheLamMel09-TOPNOC-IJ,
author = {Federico Chesani and Evelina Lamma and
Paola Mello and Marco Montali and Fabrizio Riguzzi and Sergio
Storari},
title = {Exploiting Inductive Logic Programming Techniques for Declarative
Process Mining},
journal = {LNCS Transactions on Petri Nets and Other Models of Concurrency,
{ToPNoC} {II}},
year = {2009},
publisher = {Springer},
address = {Heidelberg, \Germany},
note = {The original publication is available at \url{http://www.springerlink.com}},
series = {Lecture Notes on Computer Science},
volume = {5460},
pages = {278--295},
doi = {10.1007/978-3-642-00899-3_16},
issn = {1867-7193},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/CheLamMel-TOPNOC09.pdf},
url = {http://www.springerlink.com/content/c4j2k38675588759/},
abstract = {In the last few years, there has been a growing interest in the
adoption of declarative paradigms for modeling and verifying process
models. These paradigms provide an abstract and human understandable
way of specifying constraints that must hold among activities
executions rather than focusing on a specific procedural solution.
Mining such declarative descriptions is still an open challenge. In
this paper, we present a logic-based approach for tackling this
problem. It relies on Inductive Logic Programming techniques and,
in particular, on a modified version of the Inductive Constraint
Logic algorithm. We investigate how, by properly tuning the learning
algorithm, the approach can be adopted to mine models expressed in
the ConDec notation, a graphical language for the declarative
specification of business processes. Then, we sketch how such a
mining framework has been concretely implemented as a ProM plug-in
called DecMiner. We finally discuss the effectiveness of the
approach by means of an example which shows the ability of the
language to model concurrent activities and of DecMiner to learn
such a model.},
keywords = {Process Mining, Inductive Logic Programming, Declarative Process Languages},
copyright = {Springer}
}
@article{StoRigLam09-IDA-IJ,
author = {Sergio Storari and Fabrizio Riguzzi and Evelina Lamma},
title = {Exploiting Association and Correlation Rules Parameters for Learning
Bayesian Networks},
journal = {Intelligent Data Analysis},
year = {2009},
pages = { 689--701},
publisher = {{IOS} Press},
volume = {13},
issue = {5},
address = {Amsterdam, \TheNetherlands},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/StoRigLam-IDA09.pdf},
doi = {10.3233/IDA-2009-0388},
abstract = { In data mining, association and correlation rules
are inferred from data in order to highlight statistical dependencies among
attributes. The metrics defined for evaluating these rules can be exploited to
score relationships between attributes in Bayesian network learning. In this
paper, we propose two novel methods for learning Bayesian networks from data
that are
based on the K2 learning algorithm and that improve it by exploiting parameters
normally defined for association and correlation rules.
In particular, we propose the algorithms K2-Lift and K2-$X^{2}$, that exploit
the lift metric and the $X^2$ metric respectively. We compare
K2\--Lift, K2-$X^{2}$ with K2 on artificial data and on
three test Bayesian networks. The experiments show that both our algorithms
improve K2 with respect to the quality of the
learned network. Moreover, a comparison of K2\--Lift and K2-$X^{2}$ with a
genetic algorithm approach on two benchmark networks show superior results on
one network and comparable results on the other.},
keywords = {Bayesian Networks Learning, K2, Association Rules, Correlation
Rules},
copyright = {Sergio Storari, Fabrizio Riguzzi and Evelina Lamma, exclusively licensed to {IOS} Press}
}
@inproceedings{AlbGavLam09-CEUR-NW,
author = {Marco Alberti and
Marco Gavanelli and
Evelina Lamma and
Fabrizio Riguzzi and
Sergio Storari },
editor = {Matteo Baldoni and
Cristina Baroglio},
booktitle = {Il Milione (i.e. $2^6$, June 3rd 2008)
A Journey in the Computational Logic in Italy, Proceedings of the Day Dedicated to Prof. {Alberto Martelli}
Turin, Italy, June 3, 2008},
title = {Inducing Specification of Interaction Protocols and Business Processes and Proving their Properties},
year = {2009},
abstract = {In this paper, we overview our recent research
activity concerning the induction of Logic Programming
specifications, and the proof of their properties via Abductive
Logic Programming. Both the inductive and abductive tools here
briefly described have been applied to respectively learn and verify
(properties of) interaction protocols in multi-agent systems, Web
service choreographies, careflows and business processes.},
pdf = {http://ceur-ws.org/Vol-487/paper6.pdf},
series = {CEUR Workshop Proceedings},
publisher = {Sun {SITE} Central Europe},
issn = {1613-0073},
volume = {487},
pages = {32-37},
address = {Aachen, \Germany},
keywords = {Business Process Management, Logic Programming}
}
@inproceedings{CheMelMon-BPI08-IW,
title = {Checking Compliance of Execution Traces to Business Rules},
author = {Federico Chesani and Paola Mello and Marco Montali and Fabrizio Riguzzi
and Maurizio Sebastianis and Sergio Storari},
booktitle = {Proceedings of the 4th Workshop on Business Process Intelligence (BPI 08)},
year = {2009},
series = { Lecture Notes in Business Information Processing},
publisher = {Springer},
note = {The original publication is available at \url{http://www.springerlink.com}},
volume = {17},
pages = {129--140},
address = {Heidelberg, \Germany},
abstract = {Complex and flexible business processes are critical not only because
they are difficult to handle, but also because they often tend to loose their
intelligibility. Verifying compliance of complex and flexible processes becomes
therefore a fundamental requirement. We propose a framework for performing
compliance checking of process execution traces w.r.t.~expressive reactive
business rules, tailored to the MXML meta-model. Rules are mapped to Logic
Programming, using Prolog to classify execution traces as compliant/non-compliant.
We show how different rule templates, inspired by the ConDec language, can be
easily specified and then customized in the context of a real industrial case
study. We finally describe how the proposed language and its underlying
a-posteriori reasoning technique have been concretely implemented as a ProM
analysis plug-in.},
pdf = {http://ml.unife.it/wp-content/uploads/Papers/CheMelMon-BPI08.pdf},
doi = {10.1007/978-3-642-00328-8_13},
url = {http://www.springerlink.com/content/uh46621176654767/},
copyright = {Springer}
}
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