2009.bib

@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://ds.ing.unife.it/~friguzzi/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://ds.ing.unife.it/~friguzzi/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},
  url = {http://jigpal.oxfordjournals.org/cgi/reprint/jzp025?ijkey=picqzY6rpyU6emf&keytype=ref },
  http = {http://jigpal.oxfordjournals.org/cgi/content/abstract/jzp025?ijkey=picqzY6rpyU6emf&keytype=ref },
  keywords = {Probabilistic Logic Programming, Independent Choice Logic, Modularly acyclic programs, SLDNF-Resolution},
  copyright = {Fabrizio Riguzzi, exclusively licensed to Oxford University Press}
}
@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://ds.ing.unife.it/~friguzzi/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://ds.ing.unife.it/~friguzzi/Papers/StoRigLam-IDA09.pdf},
  doi = {10.3233/IDA-2009-0388},
  url = {http://iospress.metapress.com/content/59661362p1418230/},
  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://ds.ing.unife.it/~friguzzi/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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