% This file was created with JabRef 2.7.2.
% Encoding: UTF8

@INPROCEEDINGS{Aalst2004,
  author = {Wil M.P. van der Aalst Minseok Song},
  title = {Mining Social Networks: Uncovering Interaction Patterns in Business
	Processes},
  booktitle = {Business Process Management: Second International Conference},
  year = {2004},
  editor = {J\"org Desel and Barbara Pernici and Mathias Weske},
  volume = {3080},
  series = {Lecture Notes in Computer Science},
  pages = {244--260},
  address = {Potsdam, Germany},
  doi = {10.1007/978-3-540-25970-1_16},
  owner = {FICAR}
}

@ARTICLE{Aalst2012,
  author = {Aalst, W and Adriansyah, Arya and Medeiros, AKA},
  title = {{Process mining manifesto}},
  journal = {Business Process},
  year = {2012},
  volume = {99},
  pages = {169--194},
  number = {2},
  url = {http://www.springerlink.com/index/q8446vr556p28n20.pdf}
}

@ARTICLE{Aalst2010,
  author = {Wil M.P. van Aalst and Kees M. van Hee and Jan Martijn van Werf and
	Marc Verdonk},
  title = {Auditing 2.0: Using Process Mining to Support Tomorrow's Auditor},
  journal = {Computer},
  year = {2010},
  volume = {43},
  pages = {90--93},
  number = {3},
  address = {Los Alamitos, CA, USA},
  doi = {http://doi.ieeecomputersociety.org/10.1109/MC.2010.61},
  issn = {0018-9162},
  owner = {FICAR},
  publisher = {IEEE Computer Society},
  review = {In this work the authors explore the new way to apply auditing using
	ProM toolset. In this new way of auditing, the auditors can use to
	whole set of events to detect violation, predict the remaining processing
	time, recommend changes in executions to save execution time, etc.
	
	The auditiong is done to check whether business processes are executed
	within certain boundaries set by managers, governments, and other
	stakeholders. Violations of rules may indicate fraud, malpractice,
	risks, and inefficiencies. Since there is a set of process mining
	techniques, the work of auditor changed dramatically.}
}

@BOOK{Aalst2011b,
  title = {Process Mining: Discovery, Conformance and Enhancement of Business
	Processes},
  publisher = {Springer},
  year = {2011},
  author = {Wil M. P. van der Aalst},
  pages = {352},
  doi = {10.1007/978-3-642-19345-3},
  owner = {FICAR}
}

@ARTICLE{Aalst2003,
  author = {Wil M. P. van der Aalst and Boudewijn F. van Dongen and Joachim Herbst
	and Laura Maruster and Guido Schimm and A. J. M. M. Weijters},
  title = {Workflow mining: A survey of issues and approaches},
  journal = {Data \& Knowledge Engineering},
  year = {2003},
  volume = {47},
  pages = {237--267},
  number = {2},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  doi = {doi:10.1016/S0169-023X(03)00066-1},
  ee = {http://dx.doi.org/10.1016/S0169-023X(03)00066-1},
  owner = {FICAR}
}

@ARTICLE{Aalst2005a,
  author = {Wil M. P. van der Aalst and Ana Karla A. de Medeiros},
  title = {Process Mining and Security: Detecting Anomalous Process Executions
	and Checking Process Conformance},
  journal = {Electronic Notes in Theoretical Computer Science},
  year = {2005},
  volume = {121},
  pages = {3--21},
  number = {4},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  doi = {10.1016/j.entcs.2004.10.013},
  ee = {http://dx.doi.org/10.1016/j.entcs.2004.10.013},
  owner = {FICAR}
}

@ARTICLE{Aalst2004b,
  author = {Wil M. P. van der Aalst and A. J. M. M. Weijters},
  title = {Process mining: a research agenda},
  journal = {Computers in Industry},
  year = {2004},
  volume = {53},
  pages = {231--244},
  number = {3},
  doi = {10.1016/j.compind.2003.10.001},
  owner = {FICAR},
  page = {231-244}
}

@ARTICLE{Aalst2004a,
  author = {Wil M. P. van der Aalst and Ton Weijters and Laura Maruster},
  title = {Workflow Mining: Discovering Process Models from Event Logs.},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2004},
  volume = {16},
  pages = {1128--1142},
  number = {9},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  doi = {10.1109/TKDE.2004.47},
  ee = {http://csdl.computer.org/comp/trans/tk/2004/09/k1143abs.htm},
  owner = {FICAR},
  review = {In this paper the authors present alpha algorithm and argue about
	the class of workflows that my alpha algoritm may correctly mine.}
}

@INCOLLECTION{Accorsi2010,
  author = {Accorsi, Rafael and Wonnemann, Claus},
  title = {Auditing Workflow Executions against Dataflow Policies},
  booktitle = {Business Information Systems},
  publisher = {Springer},
  year = {2010},
  volume = {47},
  series = {Lecture Notes in Business Information Processing},
  pages = {207-217},
  address = {Berlin, Germany},
  affiliation = {Albert-Ludwigs-Universität Freiburg Department of Telematics Germany},
  doi = {10.1007/978-3-642-12814-1\_18},
  isbn = {978-3-642-12814-1},
  keyword = {Computer Science},
  owner = {FICAR},
  review = {It is a work for tackling a-posteriori analysis of workflow logs for
	the automated detection of policy violations. This paper presents
	IFAudit, a novel approach for the audit of workflow models against
	dataflow policies.
	
	
	To support the audition, it first generates a graph called "Propagation
	Graph", which is the flow of information between the subjects in
	the workflow. Then, some dataflow policies are formalized as constraints
	on the possible and undesirable relations among system subjects.
	Finally, the propagation graph is assessed whether the dataflow policies
	are complied with.
	
	
	The authors present an interesting approach for detecting violation
	of illegal data flow execution in business processes. These work
	are about a tool for forensic analysis, called RecIF, which is responsible
	for construct a graph of data flow among subjects (propagation graph)
	in a process. Then, a recorded set of polices are verified regarded
	compliance against the propagation graph constructed from the log.
	The output of processing are a set of evidences of frauds or deviations
	from polices.
	
	
	Although the work have similarities with our anomaly detection approaches
	(e.g. model construction and conformance test), there exist some
	important differences that are important to highlight. First, our
	approaches consider the assessment of control flow characteristics
	from the log for detecting anomalies. Second, they do not apply a
	direct conformance test method, since they do not use any predefined
	polices or rules to test the fitness (or conformance) among what
	is observed from the log and some polices. Finally, because our anomaly
	detection methods do not use a known normal behavior, they are more
	appropriate for dynamic and flexible business environments.}
}

@INPROCEEDINGS{Accorsi2011,
  author = {R. Accorsi and C. Wonnemann and T. Stocker},
  title = {Towards Forensic Data Flow Analysis of Business Process Logs},
  booktitle = {Sixth International Conference on IT Security Incident Management
	and IT Forensics (IMF)},
  year = {2011},
  pages = {3-20},
  address = {Stuttgart},
  doi = {10.1109/IMF.2011.13},
  owner = {FICAR},
  review = {The authors present an interesting approach for detecting violation
	of illegal data flow execution in business processes. These work
	are about a tool for forensic analysis, called RecIF, which is responsible
	for construct a graph of data flow among subjects (propagation graph)
	in a process. Then, a recorded set of polices are verified regarded
	compliance against the propagation graph constructed from the log.
	The output of processing are a set of evidences of frauds or deviations
	from polices.
	
	
	Although the work have similarities with our anomaly detection approaches
	(e.g. model construction and conformance test), there exist some
	important differences that are important to highlight. First, our
	approaches consider the assessment of control flow characteristics
	from the log for detecting anomalies. Second, they do not apply a
	direct conformance test method, since they do not use any predefined
	polices or rules to test the fitness (or conformance) among what
	is observed from the log and some polices. Finally, because our anomaly
	detection methods do not use a known normal behavior, they are more
	appropriate for dynamic and flexible business environments.}
}

@INPROCEEDINGS{Agarwal2005,
  author = {Deepak K. Agarwal},
  title = {An Empirical Bayes Approach to Detect Anomalies in Dynamic Multidimensional
	Arrays.},
  booktitle = {ICDM 2005: Proceedings of the 5th IEEE International Conference on
	Data Mining},
  year = {2005},
  pages = {26-33},
  address = {Texas, USA},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  doi = {10.1109/ICDM.2005.22},
  ee = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.22},
  owner = {fabio},
  timestamp = {2012.05.15}
}

@INPROCEEDINGS{Agrawal1998,
  author = {Rakesh Agrawal and Dimitrios Gunopulos and Frank Leymann},
  title = {Mining Process Models from Workflow Logs},
  booktitle = {EDBT '98: Proceedings of the 6th International Conference on Extending
	Database Technology},
  year = {1998},
  volume = {1377},
  series = {Lecture Notes in Computer Science},
  pages = {469--483},
  address = {Valencia, Spain},
  doi = {10.1007/BFb0101003},
  isbn = {3-540-64264-1},
  owner = {FICAR}
}

@ARTICLE{Bezerra2012,
  author = {Fabio Bezerra and Jacques Wainer},
  title = {Algorithms for anomaly detection of traces in logs of process aware
	information systems},
  journal = {Information Systems},
  year = {2012},
  volume = {-},
  pages = {In Press},
  number = {-},
  doi = {10.1016/j.is.2012.04.004},
  issn = {0306-4379},
  keywords = {Anomaly detection}
}

@ARTICLE{Bezerra2011,
  author = {Fabio Bezerra and Jacques Wainer},
  title = {Fraud detection in process aware systems},
  journal = {International Journal of Business Process Integration and Management
	(IJBPIM)},
  year = {2011},
  volume = {5},
  pages = {121--129},
  number = {2},
  doi = {10.1504/IJBPIM.2011.040204},
  owner = {FICAR},
  publisher = {Inderscience Publishers}
}

@INPROCEEDINGS{Bezerra2008,
  author = {F\'{a}bio Bezerra and Jacques Wainer},
  title = {Anomaly Detection Algorithms in Logs of Process Aware Systems},
  booktitle = {SAC '08: Proceedings of the ACM Symposium on Applied Computing},
  year = {2008},
  pages = {951--952},
  address = {Fortaleza, Ceara, Brazil},
  doi = {http://doi.acm.org/10.1145/1363686.1363904},
  isbn = {978-1-59593-753-7},
  location = {Fortaleza, Ceara, Brazil},
  owner = {FICAR}
}

@INPROCEEDINGS{Bezerra2008a,
  author = {F\'abio Bezerra and Jacques Wainer},
  title = {Anomaly Detection Algorithms in Business Process Logs},
  booktitle = {10th International Conference on Enterprise Information Systems},
  year = {2008},
  pages = {11-18},
  address = {Barcelona, Spain},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  owner = {FICAR}
}

@INCOLLECTION{Bezerra2009,
  author = {Bezerra, Fabio and Wainer, Jacques and Aalst, W. M. P.},
  title = {Anomaly Detection Using Process Mining},
  booktitle = {Enterprise, Business-Process and Information Systems Modeling},
  publisher = {Springer},
  year = {2009},
  volume = {29},
  series = {Lecture Notes in Business Information Processing},
  pages = {149--161},
  address = {Amsterdam, The Netherlands},
  doi = {10.1007/978-3-642-01862-6_13},
  isbn = {978-3-642-01862-6},
  keyword = {Computer Science},
  owner = {FICAR}
}

@ARTICLE{Bose2012,
  author = {R. P. Jagadeesh Chandra Bose and Wil M. P. van der Aalst},
  title = {Process Diagnostics Using Trace Alignment: Opportunities, Issues,
	and Challenges},
  journal = {Information Systems},
  year = {2012},
  volume = {37},
  pages = {117--141},
  number = {2},
  doi = {http://dx.doi.org/10.1016/j.is.2011.08.003},
  owner = {fabio},
  timestamp = {2012.04.24}
}

@ARTICLE{Chandola2009,
  author = {Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
  title = {Anomaly detection: A survey},
  journal = {ACM Comput. Surv.},
  year = {2009},
  volume = {41},
  pages = {1--58},
  number = {3},
  doi = {http://doi.acm.org/10.1145/1541880.1541882},
  issn = {0360-0300},
  owner = {FICAR},
  publisher = {ACM Press}
}

@ARTICLE{Cook2004,
  author = {Jonathan E. Cook and Zhidian Du and Chongbing Liu and Alexander L.
	Wolf},
  title = {Discovering models of behavior for concurrent workflows},
  journal = {Computers in Industry},
  year = {2004},
  volume = {53},
  pages = {297--319},
  number = {3},
  doi = {http://dx.doi.org/10.1016/j.compind.2003.10.005},
  issn = {0166-3615},
  owner = {FICAR},
  publisher = {Elsevier}
}

@ARTICLE{Cook1998,
  author = {Jonathan E. Cook and Alexander L. Wolf},
  title = {Discovering models of software processes from event-based data},
  journal = {ACM Trans. Softw. Eng. Methodol.},
  year = {1998},
  volume = {7},
  pages = {215--249},
  number = {3},
  doi = {http://doi.acm.org/10.1145/287000.287001},
  issn = {1049-331X},
  owner = {FICAR},
  publisher = {ACM Press}
}

@INPROCEEDINGS{Dongen2004,
  author = {B.F. van Dongen and W.M.P. van der Aalst},
  title = {Multi-phase Process Mining: Building Instance Graphs},
  booktitle = {Conceptual Modeling - ER 2004},
  year = {2004},
  volume = {3288},
  series = {Lecture Notes in Computer Science},
  pages = {362-376},
  address = {Shanghai, China},
  doi = {10.1007/b101693},
  owner = {fabio},
  timestamp = {2012.05.15}
}

@INPROCEEDINGS{Dongen2005,
  author = {B.F. van Dongen and A.K.A. de Medeiros and H.M.W. Verbeek and A.J.M.M.
	Weijters and W.M.P. van der Aalst},
  title = {The {ProM Framework}: A New Era in Process Mining Tool Support},
  booktitle = {Applications and Theory of Petri Nets},
  year = {2005},
  volume = {3536},
  series = {LNCS},
  pages = {444-454},
  address = {Miami, USA},
  publisher = {ger},
  doi = {10.1007/11494744_25},
  owner = {FICAR}
}

@INPROCEEDINGS{Donoho2004,
  author = {Donoho, Steve},
  title = {Early detection of insider trading in option markets},
  booktitle = {Proceedings of the tenth ACM SIGKDD international conference on Knowledge
	discovery and data mining},
  year = {2004},
  series = {KDD '04},
  pages = {420--429},
  address = {New York, USA},
  acmid = {1014100},
  doi = {http://doi.acm.org/10.1145/1014052.1014100},
  isbn = {1-58113-888-1},
  keywords = {behavior detection, data mining, fraud detection, insider trading},
  location = {Seattle, WA, USA},
  numpages = {10},
  owner = {FICAR}
}

@BOOK{Dumas2005,
  title = {Process-Aware Information Systems: Bridging People and Software through
	Process Technology},
  publisher = {Wiley},
  year = {2005},
  author = {Marlon Dumas and Wil van der Aalst and Arthur ter Hofstede},
  owner = {FICAR}
}

@ARTICLE{Ellis2011,
  author = {Ellis, C.A. and Kim, K. and Rembert, A. and Wainer, J.},
  title = {Investigations on Stochastic Information Control Nets},
  journal = {Information Sciences},
  year = {2011},
  volume = {194},
  pages = {120--137},
  number = {1},
  doi = {http://dx.doi.org/10.1016/j.ins.2011.07.031},
  publisher = {Elsevier}
}

@ARTICLE{Fawcett1997,
  author = {Fawcett, Tom and Provost, Foster},
  title = {Adaptive Fraud Detection},
  journal = {Data Mining and Knowledge Discovery},
  year = {1997},
  volume = {1},
  pages = {291--316},
  number = {3},
  doi = {http://dx.doi.org/10.1023/A:1009700419189},
  issue = {3},
  numpages = {26},
  owner = {FICAR},
  publisher = {Springer}
}

@ARTICLE{Goedertier2009,
  author = {Goedertier,S. and Martens,D. and Vanthienen,J. and Baesens,B.},
  title = {Robust process discovery with artificial negative events},
  journal = {Journal of Machine Learning Research},
  year = {2009},
  volume = {10},
  pages = {1305-1340},
  owner = {FICAR}
}

@ARTICLE{Greco2006,
  author = {Greco, G. and Guzzo, A. and Ponieri, L. and Sacca, D.},
  title = {Discovering expressive process models by clustering log traces},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {2006},
  volume = {18},
  pages = {1010--1027},
  number = {8},
  owner = {FICAR},
  publisher = {IEEE}
}

@ARTICLE{Hammori2006,
  author = {Markus Hammori and Joachim Herbst and Niko Kleiner},
  title = {Interactive workflow mining - requirements, concepts and implementation},
  journal = {Data \& Knowledge Engineering},
  year = {2006},
  volume = {56},
  pages = {41--63},
  number = {1},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  doi = {10.1016/j.datak.2005.02.006},
  ee = {http://dx.doi.org/10.1016/j.datak.2005.02.006},
  owner = {FICAR},
  publisher = {Elsevier},
  review = {In this work the authors present an interative approach to get a process
	model from the log. The resulting model is represented in block-structured
	manner, similarly to process mining approach presented by Schimm.
	
	
	
	This work report an implementation of the approach in the ProTo system,
	which is an alternative of InWoLve for supporting interative mining.
	Actually, ProTo uses InWoLve as its kernel.}
}

@ARTICLE{Herbst2004,
  author = {Joachim Herbst and Dimitris Karagiannis},
  title = {Workflow mining with InWoLvE},
  journal = {Computers in Industry},
  year = {2004},
  volume = {53},
  pages = {245--264},
  number = {3},
  address = {Amsterdam, The Netherlands, The Netherlands},
  doi = {http://dx.doi.org/10.1016/j.compind.2003.10.002},
  issn = {0166-3615},
  owner = {FICAR},
  publisher = {Elsevier},
  review = {In this paper the authors present a workflow mining algorithm/tool
	called InWoLve.
	
	InWoLvE solves the workflow mining task in two steps. In the first
	step it creates a stochastic activity graph from the example set
	and in the second step it transforms this stochastic activity graph
	into a well-defined workflow model.
	
	The paper reports some experiments with the tool, including experiments
	based on real data collected from a project at DaimlerChrysler.}
}

@BOOK{Larson2003,
  title = {Elementary statistics: picturing the world},
  publisher = {Prentice Hall},
  year = {2003},
  author = {Larson, R. and Farber, E.},
  isbn = {9780130655950},
  lccn = {2002016911}
}

@INPROCEEDINGS{Lee2001,
  author = {Wenke Lee and Dong Xiang},
  title = {Information-Theoretic Measures for Anomaly Detection},
  booktitle = {IEEE Symposium on Security and Privacy},
  year = {2001},
  pages = {130--143},
  address = {Oakland, California},
  doi = {http://doi.ieeecomputersociety.org/10.1109/SECPRI.2001.924294},
  owner = {FICAR}
}

@PHDTHESIS{LimaBezerra2011,
  author = {Fabio de Lima Bezerra},
  title = {Algoritmos de Detec\cc\~ao de Anomalias em Logs de Sistemas Baseados
	em Processos de Neg\'ocios},
  school = {Universidade Estadual de Campinas},
  year = {2011},
  address = {Campinas, S\~ao Paulo},
  note = {In Portuguese}
}

@INCOLLECTION{Medeiros2003,
  author = {de Medeiros, A. and van der Aalst, W. and Weijters, A.},
  title = {Workflow Mining: Current Status and Future Directions},
  booktitle = {On The Move to Meaningful Internet Systems: CoopIS, DOA, and ODBASE},
  publisher = {Springer},
  year = {2003},
  volume = {2888},
  series = {LNCS},
  pages = {389-406},
  address = {Sicily, Italy},
  affiliation = {Department of Technology Management, Eindhoven University of Technology,
	P.O. Box 513, NL-5600 MB Eindhoven, The Netherlands},
  doi = {10.1007/978-3-540-39964-3\_25},
  isbn = {978-3-540-20498-5},
  keyword = {Computer Science},
  owner = {FICAR}
}

@PHDTHESIS{Medeiros2006a,
  author = {Ana Karla Alves de Medeiros},
  title = {Genetic Process Mining},
  school = {Technische Universiteit Eindhoven},
  year = {2006},
  address = {Eindhoven, The Netherlands},
  owner = {FICAR}
}

@INPROCEEDINGS{Medeiros2006,
  author = {A. K. A. de Medeiros and A. J. M. M. Weijters and W. M. P. van der
	Aalst},
  title = {Genetic Process Mining: A Basic Approach and Its Challenges},
  booktitle = {Business Process Management Workshops},
  year = {2006},
  volume = {3812},
  series = {LNCS},
  pages = {203-215},
  address = {Nancy, France},
  month = {September},
  doi = {10.1007/11678564\_18},
  owner = {FICAR}
}

@INCOLLECTION{Neiger2009,
  author = {Neiger, Dina and Churilov, Leonid and Flitman, Andrew and Neiger,
	Dina and Churilov, Leonid and Flitman, Andrew},
  title = {Introducing Value-Focused Process Engineering},
  booktitle = {Value-Focused Business Process Engineering : a Systems Approach},
  publisher = {Springer},
  year = {2009},
  volume = {19},
  series = {Integrated Series in Information Systems},
  pages = {1--13},
  affiliation = {, Swinburne University of Technology, Melbourne, Australia},
  doi = {http://dx.doi.org/10.1007/978-0-387-09521-9\_1},
  isbn = {978-0-387-09521-9},
  keyword = {Business and Economics},
  owner = {FICAR}
}

@INPROCEEDINGS{Noble2003,
  author = {Caleb C. Noble and Diane J. Cook},
  title = {Graph-based anomaly detection},
  booktitle = {KDD '03: Proceedings of the 9th ACM SIGKDD International Conference
	on Knowledge discovery and data mining},
  year = {2003},
  pages = {631--636},
  address = {New York, USA},
  doi = {http://doi.acm.org/10.1145/956750.956831},
  isbn = {1-58113-737-0},
  location = {Washington, D.C.},
  owner = {FICAR}
}

@INPROCEEDINGS{Pandit2007,
  author = {Shashank Pandit and Duen Horng Chau and Samuel Wang and Christos
	Faloutsos},
  title = {Netprobe: a fast and scalable system for fraud detection in online
	auction networks},
  booktitle = {WWW '07: Proceedings of the 16th international conference on World
	Wide Web},
  year = {2007},
  pages = {201--210},
  address = {New York, USA},
  doi = {http://doi.acm.org/10.1145/1242572.1242600},
  isbn = {978-1-59593-654-7},
  location = {Banff, Alberta, Canada},
  owner = {FICAR}
}

@ARTICLE{Pinter2004,
  author = {Shlomit S. Pinter and Mati Golani},
  title = {Discovering workflow models from activities' lifespans},
  journal = {Computers in Industry},
  year = {2004},
  volume = {53},
  pages = {283--296},
  number = {3},
  doi = {http://dx.doi.org/10.1016/j.compind.2003.10.004},
  issn = {0166-3615},
  owner = {FICAR},
  publisher = {Elsevier}
}

@BOOK{inforet,
  title = {Information Retrieval},
  publisher = {Butterworths},
  year = {1979},
  author = {C. J. van Rijsbergen}
}

@ARTICLE{Rozinat2008,
  author = {A. Rozinat and W.M.P. van der Aalst},
  title = {Conformance checking of processes based on monitoring real behavior},
  journal = {Information Systems},
  year = {2008},
  volume = {33},
  pages = {64--95},
  number = {1},
  doi = {10.1016/j.is.2007.07.001},
  owner = {FICAR},
  publisher = {Elsevier}
}

@INPROCEEDINGS{Rozinat2005,
  author = {A. Rozinat and Wil M. P. van der Aalst},
  title = {Conformance Testing: Measuring the Fit and Appropriateness of Event
	Logs and Process Models.},
  booktitle = {Business Process Management Workshops},
  year = {2005},
  volume = {3812},
  series = {LNCS},
  pages = {163--176},
  address = {Nancy, France},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  doi = {10.1007/11678564_15},
  ee = {http://dx.doi.org/10.1007/11678564_15},
  owner = {FICAR}
}

@TECHREPORT{Rozinat2007,
  author = {A. Rozinat and A.K. Alves de Medeiros and C.W. G\"unther and A.J.M.M.
	Weijters and W.M.P. van der Aalst},
  title = {Towards an Evaluating Framework for Process Mining Algorithms},
  institution = {Technische Universiteit Eindhoven},
  year = {2007},
  owner = {FICAR},
  review = {In this technical report the authors present the importance for developing
	a framework to assess the results of process mining algorithms or
	researches.
	
	The main question is: "Among the different process models that can
	be discovered, which one is the best?".
	
	The authors argue about four different dimensions o evaluation: fitness,
	precision, generalization and structure. The fitness dimension indicates
	the portion of log that can be played by process model. The precision
	dimension indicates how much a process models precisely describes
	the log. The generalization dimension indicates how much a process
	models can generalize or play unseen instances of the log. Finaly,
	the structure dimention indicates the complexity degree that a process
	model was designed, which is difficult to assess in a objetive way.}
}

@TECHREPORT{Rubin2006,
  author = {V. Rubin and B. F. Van Dongen and E. Kindler and C. W. Günther},
  title = {Process Mining: A Two-Step Approach using Transition Systems and
	Regions},
  institution = {BPM Center},
  year = {2006},
  address = {Eindhoven},
  owner = {FICAR}
}

@INPROCEEDINGS{Sabhnani2005,
  author = {Robin Sabhnani and Daniel Neill and Andrew Moore},
  title = {Detecting Anomalous Patterns in Pharmacy Retail Data},
  booktitle = {Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly
	Detection},
  year = {2005},
  address = {Chicago, USA},
  month = {August},
  owner = {fabio},
  timestamp = {2012.05.15}
}

@ARTICLE{Schimm2004,
  author = {Guido Schimm},
  title = {Mining exact models of concurrent workflows},
  journal = {Computers in Industry},
  year = {2004},
  volume = {53},
  pages = {265--281},
  number = {3},
  doi = {http://dx.doi.org/10.1016/j.compind.2003.10.003},
  issn = {0166-3615},
  owner = {FICAR},
  publisher = {Elsevier},
  review = {In this work the author describe a process mining approach based on
	a rewriting systems. An important characteristic of this procedure
	is that it step by step aggregates data instead of merging it all
	together in one single step.
	
	The author argues that such an approach is able to mine exact models,
	which are models that meet three requirements: the first requirement
	is completeness. It means that the model should preserve all tasks
	and the dependencies between them that are present in the log. Specificity
	is the second requirement. A model meets this requirement if it does
	not introduce additional tasks or spurious dependencies between tasks.
	The third requirement is minimality. It means that the model should
	have the minimal number of elements in order to describe the workflow.
	
	The form used to represent the workflow models is another carachteristics
	of this paper. It uses a block-oriented representation. It defines
	that each workflow model consists of an arbitrary number of nested
	building blocks.}
}

@INCOLLECTION{Shishkov2009,
  author = {Shishkov, Boris and Sinderen, Marten and Verbraeck, Alexander},
  title = {Towards Flexible Inter-enterprise Collaboration: A Supply Chain Perspective},
  booktitle = {Enterprise Information Systems},
  publisher = {Springer},
  year = {2009},
  volume = {24},
  series = {Lecture Notes in Business Information Processing},
  pages = {513--527},
  address = {Milan, Italy},
  doi = {10.1007/978-3-642-01347-8_43},
  keyword = {Economics/Management Science},
  owner = {FICAR}
}

@BOOK{Steinberg2010,
  title = {Statistics Alive!},
  publisher = {SAGE Publications},
  year = {2010},
  author = {Steinberg, W.J.},
  pages = {318-324},
  edition = {Second},
  isbn = {9781412979504},
  lccn = {2010005922},
  owner = {FICAR}
}

@INCOLLECTION{Vanderfeesten2008,
  author = {Vanderfeesten, Irene and Reijers, Hajo and Mendling, Jan and van
	der Aalst, Wil and Cardoso, Jorge},
  title = {On a Quest for Good Process Models: The Cross-Connectivity Metric},
  booktitle = {Advanced Information Systems Engineering},
  publisher = {Springer},
  year = {2008},
  volume = {5074},
  series = {LNCS},
  pages = {480--494},
  address = {Montpellier, France},
  affiliation = {Technische Universiteit Eindhoven Department of Technology Management
	PO Box 513 5600 MB Eindhoven The Netherlands},
  doi = {http://dx.doi.org/10.1007/978-3-540-69534-9\_36},
  owner = {FICAR}
}

@INPROCEEDINGS{Wainer2005,
  author = {J. Wainer and K. Kim and C. A. Ellis},
  title = {A Workflow Mining Method Through Model Rewriting},
  booktitle = {Groupware: Design, Implementation, and Use: 11th International Workshop},
  year = {2005},
  volume = {3706},
  series = {LNCS},
  pages = {184--191},
  address = {Porto de Galinhas, Brazil},
  month = {Setembro},
  doi = {10.1007/11560296_14},
  owner = {FICAR}
}

@ARTICLE{Weidlich2011,
  author = {Matthias Weidlich and Artem Polyvyanyy and Nirmit Desai and Jan Mendling
	and Mathias Weske},
  title = {Process compliance analysis based on behavioural profiles},
  journal = {Information Systems},
  year = {2011},
  volume = {36},
  pages = {1009--1025},
  number = {7},
  doi = {10.1016/j.is.2011.04.002},
  issn = {0306-4379},
  keywords = {Process compliance},
  owner = {FICAR}
}

@ARTICLE{Weijters2003,
  author = {A.J.M.M. Weijters and W.M.P van der Aalst},
  title = {Rediscovering workflow models from event-based data using little
	thumb},
  journal = {Integrated Computer-Aided Engineering},
  year = {2003},
  volume = {10},
  pages = {151-162},
  number = {2},
  owner = {FICAR}
}

@TECHREPORT{Weijters2006,
  author = {A.J.M.M. Weijters and W.M.P. van der Aalst and A.K. Alves de Medeiros;},
  title = {Process Mining with the HeuristicsMiner Algorithm},
  institution = {Beta Research School for Operations Management and Logistics},
  year = {2006},
  booktitle = {Beta working papers},
  keywords = {Knowledge Discovering, Process Mining, Process Mining Benchmark, Business
	Process Intelligence},
  owner = {FICAR}
}

@INCOLLECTION{Wen2006,
  author = {Wen, Lijie and Wang, Jianmin and Sun, Jiaguang},
  title = {Detecting Implicit Dependencies Between Tasks from Event Logs},
  booktitle = {Frontiers of WWW Research and Development - APWeb 2006},
  publisher = {Springer},
  year = {2006},
  volume = {3841},
  series = {LNCS},
  pages = {591--603},
  address = {Harbin, China},
  affiliation = {School of Software, Tsinghua University, 100084, Beijing China China},
  doi = {10.1007/11610113_52},
  owner = {FICAR},
  review = {It is an extension version of alpha algorithm for solving the detection
	of implicit dependencies between tasks from event logs. This paper
	reports a formal prove of solution for mining non-free-choice constructs.}
}

@ARTICLE{Yang2006,
  author = {Wan-Shiou Yang and San-Yih Hwang},
  title = {A process-mining framework for the detection of healthcare fraud
	and abuse},
  journal = {Expert Systems with Applications},
  year = {2006},
  volume = {31},
  pages = {56-68},
  number = {1},
  doi = {doi:10.1016/j.eswa.2005.09.003},
  keywords = {Healthcare fraud; Healthcare abuse; Clinical pathways; Classification
	model; Data mining},
  owner = {FICAR},
  review = {In this paper the authors present a framework for automatic construction
	of a model for detecting healthcare fraud and abuse.
	
	Healthcare fraud has been proven to be a serious problem in some developed
	countries. It utilizes the concept of clinical pathways to facilitate
	the automatic and systematic construction of an adaptable and extensible
	detection model.
	
	The detection model is constructed from two sets of clinical instances,
	which are labeled as normal and fraudulent. They serve as the input
	of the module for discovering structure patterns. This module produces
	a set of structure patterns that have occurred frequently, which
	then serve as features of the dataset of clinical instances. Each
	clinical instance is considered an example that comprises a set of
	features and a class label (normal or fraudulent).
	
	The resultant data set is the output of a feature selection module
	that eliminates redundant and irrelevant features. Such a data set
	is used to construct the detection model, which is performed by the
	induction module. The detection model is then used to detect the
	incoming instances that are fraudulent.}
}