references.bib

%Capitalization is very important in references. Be sure to do a double {{ and }} to keep the capitalization of your text in tact.
%Also pay attention to the sorting on last name in the case of complex Dutch names. F.i.: "Aalst, W.M.P. van der and ..." makes it sort under A
@MASTERSTHESIS{MScBuijs2010,
  author = {Buijs, J.C.A.M.},
  title = {{Mapping Data Sources to XES in a Generic Way}},
  school = {{Eindhoven University of Technology}},
  year = {2010},
  file = {Master thesis Joos 2010:C\:\\Users\\jbuijs.TUE\\Documents\\PhD\\Resources\\thesis_JBuijs_final.pdf:PDF},
  keywords = {XES, event log, data, joos, master thesis},
  url = {http://www.processmining.org/_media/xesame/xesma_thesis_final.pdf}
}
@article{rousseeuw1987silhouettes,
  title={Silhouettes: a graphical aid to the interpretation and validation of cluster analysis},
  author={Rousseeuw, Peter J},
  journal={Journal of computational and applied mathematics},
  volume={20},
  pages={53--65},
  year={1987},
  publisher={Elsevier}
}

@article{thalamuthu2006evaluation,
  title={Evaluation and comparison of gene clustering methods in microarray analysis},
  author={Thalamuthu, Anbupalam and Mukhopadhyay, Indranil and Zheng, Xiaojing and Tseng, George C},
  journal={Bioinformatics},
  volume={22},
  number={19},
  pages={2405--2412},
  year={2006},
  publisher={Oxford University Press}
}

@article{dudoit2002prediction,
  title={A prediction-based resampling method for estimating the number of clusters in a dataset},
  author={Dudoit, Sandrine and Fridlyand, Jane},
  journal={Genome biology},
  volume={3},
  number={7},
  pages={research0036--1},
  year={2002},
  publisher={BioMed Central}
}


@article{iso8601,
  title={Data elements and interchange formats — Information interchange	-
Representation of dates and times},
  author={{International Organization for Standardization}},
  year={2016},
  publisher={{International Organization for Standardization}}
}

@article{Kuhlmann2003,
abstract = {In this paper we describe the work devising a new technique for role-finding to implement Role-Based Security Administration. Our results stem from industrial projects, where large-scale customers wanted to migrate to Role-Based Access Control (RBAC) based on already existing access rights patterns in their production IT-systems.},
author = {Kuhlmann, Martin and Shohat, Dalia and Schimpf, Gerhard},
doi = {10.1145/775412.775435},
isbn = {1581136811},
journal = {Proceedings of the eighth ACM symposium on Access control models and technologies  - SACMAT 2003},
pages = {179},
title = {{Role mining - revealing business roles for security administration using data mining technology}},
url = {http://portal.acm.org/citation.cfm?doid=775412.775435},
year = {2003}
}

@inproceedings{zhang2007role,
  title={Role engineering using graph optimisation},
  author={Zhang, Dana and Ramamohanarao, Kotagiri and Ebringer, Tim},
  booktitle={Proceedings of the 12th ACM symposium on Access control models and technologies},
  pages={139--144},
  year={2007},
  organization={ACM}
}

@article{Schlegelmilch2005,
abstract = {With continuously growing numbers of applications, enterprises face the problem of efficiently managing the assignment of access permissions to their users. On the one hand, security demands a tight regime on permissions; on the other hand, users need permissions to perform their tasks. Role-based access control (RBAC) has proven to be a solution to this problem but relies on a well-defined set of role definitions, a role concept for the enterprise in question. The definition of a role concept (role engineering) is a difficult task traditionally performed via interviews and workshops. However, often users already have the permissions that they need to do their jobs, and roles can be derived from these permission assignments using data mining technology, thus giving the process of role concept definition a head-start.In this paper, we present the ORCA role mining tool and its algorithm. The algorithm performs a cluster analysis on permission assignments to build a hierarchy of permission clusters and presents the results to the user in graphical form. It allows the user to interactively add expert knowledge to guide the clustering algorithm. The tool provides valuable insights into the permission structures of an enterprise and delivers an initial role hierarchy for the definition of an enterprise role concept using a bottom-up approach.},
author = {Schlegelmilch, J{\"{u}}rgen and Steffens, Ulrike},
doi = {10.1145/1063979.1064008},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Role Mining with ORCA.pdf:pdf},
isbn = {1595930450},
journal = {Proceedings of the tenth ACM symposium on Access control models and technologies - SACMAT '05},
keywords = {all or part of,cluster analysis,data mining,engineering,or hard copies of,permission to make digital,role,role definition,role hierarchy,role mining,role-based access control,this work for},
pages = {168},
title = {{Role mining with ORCA}},
url = {http://dl.acm.org/citation.cfm?id=1064008{\%}5Cnhttp://portal.acm.org/citation.cfm?doid=1063979.1064008},
year = {2005}
}



@inproceedings{coyne1996role,
  title={Role engineering},
  author={Coyne, Edward J},
  booktitle={Proceedings of the first ACM Workshop on Role-based access control},
  pages={4},
  year={1996},
  organization={ACM}
}
@inproceedings{schimpf2000role,
  title={Role-engineering critical success factors for enterprise security administration},
  author={Schimpf, Gerhard},
  booktitle={Proc. of the 16th Annual Computer Security Applications Conference (ACSAC’00)},
  year={2000}
}

@inproceedings{epstein2001engineering,
  title={Engineering of role/permission assignments},
  author={Epstein, Pete and Sandhu, Ravi},
  booktitle={Computer Security Applications Conference, 2001. ACSAC 2001. Proceedings 17th Annual},
  pages={127--136},
  year={2001},
  organization={IEEE}
}

@inproceedings{kuhlmann2003role,
  title={Role mining-revealing business roles for security administration using data mining technology},
  author={Kuhlmann, Martin and Shohat, Dalia and Schimpf, Gerhard},
  booktitle={Proceedings of the eighth ACM symposium on Access control models and technologies},
  pages={179--186},
  year={2003},
  organization={ACM}
}

@inproceedings{roeckle2000role,
  title={Role-finding/role-engineering (panel session)},
  author={Roeckle, Haio},
  booktitle={Proceedings of the fifth ACM workshop on Role-based access control},
  pages={68},
  year={2000},
  organization={ACM}
}

@article{aalst2003workflow,
  title={Workflow patterns},
  author={van Der Aalst, Wil MP and Ter Hofstede, Arthur HM and Kiepuszewski, Bartek and Barros, Alistair P},
  journal={Distributed and parallel databases},
  volume={14},
  number={1},
  pages={5--51},
  year={2003},
  publisher={Springer}
}

@book{russell2016workflow,
  title={Workflow patterns: the definitive guide},
  author={Russell, Nick and van der Aalst, Wil MP and ter Hofstede, Arthur HM},
  year={2016},
  publisher={MIT Press}
}

@article{dac,
 author = {Harrison, Michael A. and Ruzzo, Walter L. and Ullman, Jeffrey D.},
 title = {Protection in Operating Systems},
 journal = {Commun. ACM},
 issue_date = {Aug. 1976},
 volume = {19},
 number = {8},
 month = aug,
 year = {1976},
 issn = {0001-0782},
 pages = {461--471},
 numpages = {11},
 url = {http://doi.acm.org/10.1145/360303.360333},
 doi = {10.1145/360303.360333},
 acmid = {360333},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Turing machine, decidability, operating system, protection, protection system},
} 

@article{mac,
  title={Lattice-based access control models},
  author={Sandhu, Ravi S.},
  journal={Computer},
  volume={26},
  number={11},
  pages={9--19},
  year={1993},
  publisher={IEEE}
}

@misc{aalst2016business,
  title={Business process management},
  author={Van Der Aalst, Wil MP and La Rosa, Marcello and Santoro, Fl{\'a}via Maria},
  year={2016},
  publisher={Springer}
}

@article{yawl,
  title={YAWL: yet another workflow language},
  author={Van Der Aalst, Wil MP and Ter Hofstede, Arthur HM},
  journal={Information systems},
  volume={30},
  number={4},
  pages={245--275},
  year={2005},
  publisher={Elsevier}
}

@article{petrinets,
  title={Petri net theory and the modeling of systems},
  author={Peterson, James L},
  year={1981},
  publisher={Prentice Hall PTR}
}

@misc{bpmn2,
  title={Business Process Model and Notation (BPMN) version 2.0},
  author={Object Management Group (OMG)},
  year={2011}
}

@book{dumas2013fundamentals,
  title={Fundamentals of business process management},
  author={Dumas, Marlon and La Rosa, Marcello and Mendling, Jan and Reijers, Hajo A and others},
  volume={1},
  year={2013},
  publisher={Springer}
}

@book{hofstede2009modern,
  title={Modern Business Process Automation: YAWL and its support environment},
  author={ter Hofstede, Arthur HM and van der Aalst, Wil MP and Adams, Michael and Russell, Nick},
  year={2009},
  publisher={Springer Science \& Business Media}
}

@article{rbac,
  title={Role-based access control models},
  author={Sandhu, Ravi S and Coyne, Edward J and Feinstein, Hal L and Youman, Charles E},
  journal={Computer},
  volume={29},
  number={2},
  pages={38--47},
  year={1996},
  publisher={IEEE}
}

@incollection{rbac2,
  title={Access Control Policies, Models, and Mechanisms},
  author={di Vimercati, Sabrina De Capitani},
  booktitle={Encyclopedia of Cryptography and Security},
  pages={13--14},
  year={2011},
  publisher={Springer}
}

@MASTERSTHESIS{MScNugteren2010,
  author = {Nugteren, G.M.},
  title = {{Process Model Simplification}},
  school = {{Eindhoven University of Technology}},
  year = {2010},
  url = {http://alexandria.tue.nl/extra1/afstversl/wsk-i/nugteren2010.pdf}
}

@article{Rodrigues2017,
author = {Rodrigues, Ariane and Almeida, Cassio and Saraiva, Daniel and Moreira, Felipe and Spyrides, Georges and Varela, Guilherme and Krieger, Gustavo and Peres, Igor and Dantas, Leila and Lana, Mauricio and Alves, Odair and Fran{\c{c}}a, Rafael and Neira, Ricardo and Gonzalez, Sonia and Fernandes, William and Barbosa, Simone and Poggi, Marcus and {and H{\'{e}}lio Lopes}},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Stairway to value - mining a loan application process.pdf:pdf},
keywords = {descriptive analytics,diagnostic analytics,process mining},
title = {{Stairway to value: {\{}M{\}}ining the loan application process}},
year = {2017}
}
@article{Povalyaeva2017,
author = {Povalyaeva, Elizaveta and Khamitov, Ismail and Fomenko, Artyom},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/BPIC 2017 - Density Analysis of the Interaction With Clients.pdf:pdf},
title = {{BPIC 2017 : Density Analysis of the Interaction With Clients}},
year = {2017}
}
@article{Blevi2017,
abstract = {The BPI Challenge is an annual process mining competition, in which the participants are provided with a real-life event log. This year's event log includes all events related to the loan application process of a Dutch Financial Institute. The data includes three types of information, states of the application, states of the offer(s) belonging to the application and states of the workitem(s) beloging to the application. The process owner wants to gain insight in the throughput times per part of the process, the impact of information requests on the outcome of the process and the difference in process patterns based on the number of created offers. Therefore, we analyzed the event logs using a combination of process mining techniques and tools, including SQL, Power BI, Disco and ProM.},
author = {Blevi, Liese and Delporte, Lucie and Robbrecht, Julie},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Process mining on the loan application process of a Dutch Financial Institute .pdf:pdf},
keywords = {BPI Challenge,Event logs,Loan Application Process,Process Mining},
title = {{Process mining on the loan application process of a Dutch Financial Institute BPI Challenge 2017}},
year = {2017}
}
@article{VanDerAalst2011,
abstract = {Process mining allows for the automated discovery of process models from event logs. These models provide insights and enable various types of model-based analysis. This paper demonstrates that the discovered process models can be extended with information to predict the completion time of running instances. There are many scenarios where it is useful to have reliable time predictions. For example, when a customer phones her insurance company for information about her insurance claim, she can be given an estimate for the remaining processing time. In order to do this, we provide a configurable approach to construct a process model, augment this model with time information learned from earlier instances, and use this to predict e.g., the completion time. To provide meaningful time predictions we use a configurable set of abstractions that allow for a good balance between "overfitting" and "underfitting". The approach has been implemented in ProM and through several experiments using real-life event logs we demonstrate its applicability. {\textcopyright} 2010 Elsevier B.V. All rights reserved.},
author = {{Van Der Aalst}, W. M.P. and Schonenberg, M. H. and Song, M.},
doi = {10.1016/j.is.2010.09.001},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Time prediction based on process mining.pdf:pdf},
issn = {03064379},
journal = {Information Systems},
keywords = {Business intelligence,Business process management,Performance analysis,Process mining,Time prediction},
number = {2},
pages = {450--475},
publisher = {Elsevier},
title = {{Time prediction based on process mining}},
url = {http://dx.doi.org/10.1016/j.is.2010.09.001},
volume = {36},
year = {2011}
}
@article{Russell2004,
abstract = {Workflow systems seek to provide an implementation vehicle for complex, recurring business processes. Notwithstanding this common objective, there are a variety of distinct features offered by commercial workflow management systems. These differences result in significant variations in the ability of distinct tools to represent and implement the plethora of requirements that may arise in contemporary business processes. Many of these requirements recur quite frequently during the requirements analysis activity for workflow systems and abstractions of these requirements serve as a useful means of identifying the key components of workflow languages. Previous work has identified a number of Workflow Control Patterns and Workflow Data Patterns, which characterize the range of control flow and data constructs that might be encountered when modelling and analysing workflows. In this paper, we describe a series of Workflow Resource Patterns that aim to capture the various ways in which resources are represented and utilized in workflows. By delineating these Patterns in a form that is independent of specific workflow technologies and modelling languages, we are able to provide a comprehensive treatment of the resource perspective and we subsequently use these Patterns as the basis for a detailed comparison of a number of commercially available workflow management systems and business process modelling languages.},
author = {Russell, Nick and Hofstede, Arthur H M and Edmond, David},
doi = {10.1007/11431855_16},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Workflow resource patterns.pdf:pdf},
isbn = {WP 127},
issn = {03029743},
journal = {Business},
keywords = {business process modelling,flow systems,organisational modelling,patterns,resource modelling,work},
number = {5446},
pages = {13--17},
title = {{Workflow resource patterns}},
url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.77.8969{\&}rep=rep1{\&}type=pdf},
volume = {3520},
year = {2004}
}
@article{Huang2011a,
abstract = {Efficient resource allocation is a complex and dynamic task in business process management. Although a wide variety of mechanisms are emerging to support resource allocation in business process execution, these approaches do not consider performance optimization. This paper introduces a mechanism in which the resource allocation optimization problem is modeled as Markov decision processes and solved using reinforcement learning. The proposed mechanism observes its environment to learn appropriate policies which optimize resource allocation in business process execution. The experimental results indicate that the proposed approach outperforms well known heuristic or hand-coded strategies, and may improve the current state of business process management. {\textcopyright} 2010 Elsevier B.V. All rights reserved.},
author = {Huang, Zhengxing and {Van Der Aalst}, W. M.P. and Lu, Xudong and Duan, Huilong},
doi = {10.1016/j.datak.2010.09.002},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Reinforcement learning based resource allocation in business.pdf:pdf},
isbn = {0169023X},
issn = {0169023X},
journal = {Data and Knowledge Engineering},
keywords = {Business process,Markov decision process,Q-learning,Reinforcement learning,Resource allocation},
number = {1},
pages = {127--145},
publisher = {Elsevier B.V.},
title = {{Reinforcement learning based resource allocation in business process management}},
url = {http://dx.doi.org/10.1016/j.datak.2010.09.002},
volume = {70},
year = {2011}
}
@article{Pika2015,
abstract = {TESIS;HRM},
author = {Pika, Anastasiia},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Mining Process Risks and Resource Profiles.pdf:pdf},
journal = {BSc. (Computer science), MSc. (Computer science)},
title = {{Mining Process Risks and Resource Profiles}},
url = {https://eprints.qut.edu.au/86079/1/Anastasiia{\_}Pika{\_}Thesis.pdf},
year = {2015}
}
@article{A.a2014,
abstract = {Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes' executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework's applicability is demonstrated using an event log from a German bank. {\textcopyright} 2014 Springer International Publishing.},
author = {A.a, Pika and M.T.a, Wynn and C.J.a, Fidge and B, Ter Hofstede A.H.M.a and M.c, Leyer and B, Van Der Aalst W.M.P.a},
doi = {10.1007/978-3-319-07881-6_38},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/An extensible framework for analysing resource behaviour using event.pdf:pdf},
isbn = {9783319078809},
issn = {03029743},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
keywords = {Business Process; Employee performance; Extensibl,Human resource management; Query languages; System,Information systems},
pages = {564--579},
title = {{An extensible framework for analysing resource behaviour using event logs}},
url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84903194928{\&}partnerID=40{\&}md5=346f8bb299db652830b6bd3926fa595e},
volume = {8484 LNCS},
year = {2014}
}
@article{Senderovich2015,
abstract = {Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today's economies. Conceptual models of such service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. Automatic mining of such operational models becomes feasible in the presence of event-data traces. In this work, we target the mining of models that assume a resource-driven perspective and focus on queueing effects. We propose a solution for the discovery and validation problem of scheduled service processes - processes with a predefined schedule for the execution of activities. Our prime example for such processes are complex outpatient treatments that follow prior appointments. Given a process schedule and data recorded during process execution, we show how to discover Fork/Join networks, a specific class of queueing networks, and how to assess their operational validity. We evaluate our approach with a real-world dataset comprising clinical pathways of outpatient clinics, recorded by a real-time location system (RTLS). We demonstrate the value of the approach by identifying and explaining operational bottlenecks. {\textcopyright} Springer International Publishing Switzerland 2015.},
author = {Senderovich, Arik and Weidlich, Matthias and Gal, Avigdor and Mandelbaum, Avishai and Kadish, Sarah and Bunnell, Craig A.},
doi = {10.1007/978-3-319-19069-3_26},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Discovery and Validation of Queueing Networks in Scheduled Processes.pdf:pdf},
isbn = {9783319190686},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {417--433},
title = {{Discovery and validation of queueing networks in scheduled processes}},
volume = {9097},
year = {2015}
}
@article{Senderovich2015a,
abstract = {{\textcopyright} Springer International Publishing Switzerland 2015.The performance of scheduled business processes is of central importance for services and manufacturing systems. However, current techniques for performance analysis do not take both queueing semantics and the process perspective into account. In this work, we address this gap by developing a novel method for utilizing rich process logs to analyze performance of scheduled processes. The proposed method combines simulation, queueing analytics, and statistical methods. At the heart of our approach is the discovery of an individual-case model from data, based on an extension of the Colored Petri Nets formalism. The resulting model can be simulated to answer performance queries, yet it is computational inefficient. To reduce the computational cost, the discovered model is projected into Queueing Networks, a formalism that enables efficient performance analytics. The projection is facilitated by a sequence of folding operations that alter the structure and dynamics of the Petri Net model. We evaluate the approach with a real-world dataset from Dana-Farber Cancer Institute, a large outpatient cancer hospital in the United States.},
author = {Senderovich, Arik and Rogge-Solti, Andreas and Gal, Avigdor and Mendling, Jan and Mandelbaum, Avishai and Kadish, Sarah and Bunnell, Craig A.},
doi = {10.1007/978-3-319-23063-4_3},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Data-Driven Performance Analysis of Scheduled Processes.pdf:pdf},
isbn = {9783319230627},
issn = {16113349},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
pages = {35--52},
title = {{Data-driven performance analysis of scheduled processes}},
volume = {9253},
year = {2015}
}
@misc{,
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/International Conference on Business Process Management.epub:epub},
title = {{International Conference on Business Process Management}},
year = {2006}
}
@article{Senderovich,
author = {Senderovich, Arik},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Queue Mining - Service Perspectives in Process Mining.pdf:pdf},
number = {Section 2},
title = {{Queue Mining : Service Perspectives in Process Mining ( Extended Abstract )}}
}
@book{Mannhardt2018,
author = {Mannhardt, Felix},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Multi-perspective process mining.pdf:pdf},
isbn = {9789038644387},
keywords = {Conformance checking,Event logs,Multiple perspectives,Process discovery,Process mining},
pages = {425},
title = {{Multi-perspective Process Mining Felix}},
year = {2018}
}
@book{Nakatumba2013,
author = {Nakatumba, Joyce},
booktitle = {Eindhoven University of Technology},
doi = {10.6100/IR760115},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Resource-aware business process management.pdf:pdf},
isbn = {9789038634722},
title = {{Resource-aware business process management: analysis and support}},
year = {2013}
}
@article{DeLeoni2012,
abstract = {Process mining is not restricted to process discovery and also includes conformance checking, i.e., checking whether observed behavior recorded in the event log matches modeled behavior. Many organizations have descriptive or nor- mative models that do not adequately describe the actual processes. Therefore, a variety of techniques for conformance checking have been proposed. However, all of these techniques focus on the control-flow and abstract from data and re- sources. This paper describes an approach that aligns event log and model while taking all perspectives into account (i.e., also data and resources). This way it is possible to quantify conformance and analyze differences between model and re- ality. The approach has been implemented in ProM and evaluated using a variety of model-log combinations.},
author = {{De Leoni}, Massimiliano and {Van Der Aalst}, Wil M P and {Van Dongen}, Boudewijn F.},
doi = {10.1007/978-3-642-30359-3_5},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Data- and resource-aware conformance checking of business processes..pdf:pdf},
isbn = {9783642303586},
issn = {18651348},
journal = {Lecture Notes in Business Information Processing},
pages = {48--59},
title = {{Data- and resource-aware conformance checking of business processes}},
volume = {117 LNBIP},
year = {2012}
}
@article{Mannhardt2016,
abstract = {Organizations maintain process models that describe or prescribe how cases (e.g., orders) are handled. However, reality may not agree with what is modeled. Conformance checking techniques reveal and diagnose differences between the behavior that is modeled and what is observed. Existing conformance checking approaches tend to focus on the control-flow in a process, while abstracting from data dependencies, resource assignments, and time constraints. Even in those situations when other perspectives are considered, the control-flow is aligned first, i.e., priority is given to this perspective. Data dependencies, resource assignments, and time constraints are only considered as “second-class citizens”, which may lead to misleading conformance diagnostics. For example, a data attribute may provide strong evidence that the wrong activity was executed. Existing techniques will still diagnose the data-flow as deviating, whereas our approach will indeed point out that the control-flow is deviating. In this paper, a novel algorithm is proposed that balances the deviations with respect to all these perspectives based on a customizable cost function. Evaluations using both synthetic and real data sets show that a multi-perspective approach is indeed feasible and may help to circumvent misleading results as generated by classical single-perspective or staged approaches.},
author = {Mannhardt, Felix and de Leoni, Massimiliano and Reijers, Hajo A. and van der Aalst, Wil M P},
doi = {10.1007/s00607-015-0441-1},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Balanced multi-perspective checking of process.pdf:pdf},
isbn = {0010-485X},
issn = {0010485X},
journal = {Computing},
keywords = {Data Petri nets,Log-process alignment,Multi-perspective conformance checking,Process mining},
number = {4},
pages = {407--437},
title = {{Balanced multi-perspective checking of process conformance}},
volume = {98},
year = {2016}
}
@article{Rojas2016,
author = {Rojas, Eric and Sepulveda, Marcos},
doi = {10.1007/978-3-319-42887-1},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/A Framework for Recommending Resource Allocation Based on Process Mining.pdf:pdf},
isbn = {978-3-319-42886-4},
number = {December 2017},
title = {{Business Process Management Workshops}},
url = {http://link.springer.com/10.1007/978-3-319-42887-1},
volume = {256},
year = {2016}
}
@article{Zhao2015,
abstract = {Sentiment classification on product reviews has become a popular topic in the research community. In this paper, we propose an approach to generating multi-unigram features to enhance a negation-aware Naive Bayes classifier for sentiment classification on sentences of product reviews. We coin the term "multi-unigram feature" to represent a new kind of features that are generated in our proposed algorithm with capturing high-frequently co-appeared unigram features in the training data. We further make the classifier aware of negation expressions in the training and classification process to eliminate the confusions of the classifier that is caused by negation expressions within sentences. Extensive experiments on a human-labeled data set not only qualitatively demonstrate good quality of the generated multi-unigram features but also quantitatively show that our proposed approach beats three baseline methods. Experiments on impact analysis of parameters illustrate that our proposed approach stably outperforms the baseline methods. {\textcopyright} 2010 Springer-Verlag Berlin Heidelberg.},
author = {Zhao, Weidong and Yang, Liu and Liu, Haitao and Wu, Ran},
doi = {10.1007/978-3-319-22053-6},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/The Optimization of Resource Allocation Based.pdf:pdf},
isbn = {978-3-319-22052-9},
issn = {03029743},
keywords = {optimization models,process mining,process performance,resource allocation,resource coordination},
pages = {341--353},
title = {{Advanced Intelligent Computing Theories and Applications}},
url = {http://link.springer.com/10.1007/978-3-319-22053-6},
volume = {9227},
year = {2015}
}
@article{Huang2011,
abstract = {Resource allocation is of great importance for business process management. In business process execution, a set of rules that specify resource allocation is always implied. Although many approaches have been offered to support resource allocation, they are not sufficient to derive interesting resource allocation rules which ensure that each activity is performed by suitable resource. Hence, this paper introduces an association rule mining based approach to mine interesting resource allocation rules from event log. The idea is to concern the ordered correlations between items in event log, and then to present two efficient algorithms to mine real "interesting" rules. The event log of radiology CT-scan examination process provided by the Chinese Huzhou hospital is used to verify the proposed approach. The evaluation results showed that the proposed approach not only is able to extract the rules more efficient and much faster, but also can discover more important resource allocation rules. {\textcopyright} 2011 Published by Elsevier Ltd.},
author = {Huang, Zhengxing and Lu, Xudong and Duan, Huilong},
doi = {10.1016/j.eswa.2011.01.146},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Mining association rules to support resource allocation in business process management.pdf:pdf},
isbn = {0957-4174},
issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Association rules,Business process management,Data mining,Resource allocation},
number = {8},
pages = {9483--9490},
publisher = {Elsevier Ltd},
title = {{Mining association rules to support resource allocation in business process management}},
url = {http://dx.doi.org/10.1016/j.eswa.2011.01.146},
volume = {38},
year = {2011}
}
@article{Senderovich2015b,
abstract = {Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Information recorded by systems during the operation of these processes provides an angle for operational process analysis, commonly referred to as process mining. In this work, we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We present predictors that treat queues as first-class citizens and either enhance existing regression-based techniques for process mining or are directly grounded in queueing theory. In particular, our predictors target multi-class service processes, in which requests are classified by a type that influences their processing. Further, we introduce queue mining techniques that derive the predictors from event logs recorded by an information system during process execution. Our evaluation based on large real-world datasets, from the telecommunications and financial sectors, shows that our techniques yield accurate online predictions of case delay and drastically improve over predictors neglecting the queueing perspective.},
author = {Senderovich, Arik and Weidlich, Matthias and Gal, Avigdor and Mandelbaum, Avishai},
doi = {10.1016/j.is.2015.03.010},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Queue Mining for Delay Prediction in Multi-Class Service Processes.pdf:pdf},
isbn = {0306-4379},
issn = {03064379},
journal = {Information Systems},
keywords = {Delay prediction,Process mining,Queue mining,Queueing theory},
pages = {278--295},
title = {{Queue mining for delay prediction in multi-class service processes}},
volume = {53},
year = {2015}
}
@article{Senderovich2014,
author = {Senderovich, Arik and Weidlich, Matthias and Gal, Avigdor and Mandelbaum, Avishai},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Queue Mining - Predicting Delays in Service Processes.pdf:pdf},
journal = {Advanced Information Systems Engineering},
pages = {42--57},
title = {{Queue mining--predicting delays in service processes}},
year = {2014}
}
@article{Liu2012,
abstract = {Currently, workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation (also known as "staff assignment") operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Na{\"{i}}ve Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations. {\textcopyright} 2012 Elsevier B.V. All rights reserved.},
author = {Liu, Tingyu and Cheng, Yalong and Ni, Zhonghua},
doi = {10.1016/j.knosys.2012.05.010},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Mining event logs to support workflow resource allocation.pdf:pdf},
isbn = {0950-7051},
issn = {09507051},
journal = {Knowledge-Based Systems},
keywords = {Association rules,Data mining,Process mining,Resource allocation,Workflow},
pages = {320--331},
publisher = {Elsevier B.V.},
title = {{Mining event logs to support workflow resource allocation}},
url = {http://dx.doi.org/10.1016/j.knosys.2012.05.010},
volume = {35},
year = {2012}
}
@book{ChristophRosenkranz;StefanSeidel;JanMendling;MarkusSchafermeyer;JanRecker2009,
author = {{Christoph Rosenkranz; Stefan Seidel; Jan Mendling; Markus Sch{\"{a}}fermeyer; Jan Recker}},
booktitle = {Business Process Management Workshops},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Analyzing Resource Behavior Using Process Mining.pdf:pdf},
isbn = {9783642229701},
pages = {53--66},
title = {{Analyzing Resource Behavior Using Process Mining}},
url = {http://lhrgateway.nu.edu.pk/articles/Metrics for Process Models.pdf},
year = {2009}
}
@article{This2017,
author = {This, Disclaimer},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/850545-1.pdf:pdf},
title = {{Automated algorithm configuration for ILP-based process discovery Automated Algorithm Configuration for ILP-based Process Discovery Eindhoven University of Technology}},
year = {2017}
}
@article{Peters2016,
author = {Peters, SPF},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/855860-1.pdf:pdf},
title = {{Throughput time and time window estimation for business processes using historical data}},
url = {https://pure.tue.nl/ws/files/46946469/855860-1.pdf},
year = {2016}
}
@article{This2016,
author = {This, Disclaimer},
file = {:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/This - 2016 - Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis of.pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/This - 2016 - Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis(2).pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/This - 2016 - Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis(3).pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/This - 2016 - Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis(4).pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/This - 2016 - Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis(5).pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/This - 2016 - Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis(6).pdf:pdf;:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/Pennekamp{\_}2016.pdf:pdf},
title = {{Eindhoven University of Technology Department of Mathematics and Computer Science Business Process Hierarchies Analysis of hierarchical information in path-based event logs By : Kevin Pennekamp}},
year = {2016}
}
@article{M2017,
author = {M, Polderdijk},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/Master{\_}thesis{\_}Melanie{\_}Polderdijk.pdf:pdf},
title = {{processes Extending BPMN for Analysis of Human Physical Risk Factors in Manufacturing Processes BSc . Technology Management Master of Science}},
year = {2017}
}
@article{Approach2015,
author = {Approach, A Visual},
file = {:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Approach - 2015 - Comparing Business Processes using Event Data.pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Approach - 2015 - Comparing Business Processes using Event Data(2).pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Approach - 2015 - Comparing Business Processes using Event Data(3).pdf:pdf;:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/793609-1.pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Approach - 2015 - Comparing Business Processes using Event Data(4).pdf:pdf},
keywords = {process mining, model comparison, event log, event},
number = {February},
title = {{Comparing Business Processes using Event Data}},
year = {2015}
}
@article{This2017a,
author = {This, Disclaimer},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/789180-1.pdf:pdf},
title = {{Managing business process variability in artifact-centric BPM Managing Business Process Variability in Artifact-centric BPM}},
year = {2017}
}
@misc{,
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
doi = {10.1007/s11947-009-0181-3},
eprint = {arXiv:1011.1669v3},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/801551-1.pdf:pdf},
isbn = {0021-9630 (Print)$\backslash$r0021-9630 (Linking)},
issn = {1935-5130},
pmid = {17355401},
title = {{HoPaSa09b.pdf}}
}
@article{This2016a,
author = {This, Disclaimer},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/20170926{\_}bb{\_}855659{\_}Arvaniti{\_}2016.pdf:pdf},
title = {{discovering unusual financial transactions Data Mining Journal Entries : Discovering unusual financial transactions Vasiliki Arvaniti In partial fulfillment of the requirements for the degree of}},
year = {2016}
}
@article{Stevandy2017,
author = {Stevandy, Christian and Stevandy, Christian},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/799494-1.pdf:pdf;:C$\backslash$:/Users/mike/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Stevandy, Stevandy - 2017 - Combining event logs with supplementary events Combining Event Logs with Supplementary Events.pdf:pdf},
title = {{Combining event logs with supplementary events Combining Event Logs with Supplementary Events}},
year = {2017}
}
@article{Brochenin2017,
abstract = {We present a novel learning analytics approach, for analyzing the usage of resources in MOOCs. Our target stakeholders are the course designers who aim to evaluate their learning materials. In order to gain insight into the way educational resources are used, we view dropout behaviour in an atypical manner: Instead of using it as an indicator of failure, we use it as a mean to compute other features. For this purpose, we developed a prototype, called RUAF, that can be applied to the data format provided by FutureLearn. As a proof of concept, we perform a study by applying this tool to the interaction data of learners from four MOOCs. We also study the quality of our computations, by comparing them to existing process mining approaches. We present results that highlight patterns showing how learners use resources. We also show examples of practical conclusions a course designer may benefit from.},
archivePrefix = {arXiv},
arxivId = {1710.05917},
author = {Brochenin, Remi and Buijs, Joos and Vahdat, Mehrnoosh and van der Aalst, Wil},
eprint = {1710.05917},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Resource Usage Analysis from a Different Perspective on MOOC Dropout.pdf:pdf},
keywords = {educational data mining,fu-,learning analytics,mooc},
pages = {1--30},
title = {{Resource Usage Analysis from a Different Perspective on MOOC Dropout}},
url = {http://arxiv.org/abs/1710.05917},
year = {2017}
}
@article{Pika2017,
abstract = {In most business processes, several activities need to be executed by human resources and cannot be fully automated. To evaluate resource performance and identify best practices as well as opportunities for im-provement, managers need objective information about resource behaviors. Companies often use information systems to support their processes, and these systems record information about process execution in event logs. We present a framework for analyzing and evaluating resource behavior through mining such event logs. The framework provides (1) a method for extracting descriptive information about resource skills, utilization, preferences, productivity, and collaboration patterns; (2) a method for analyzing relationships between different resource behaviors and outcomes; and (3) a method for evaluating the overall resource productivity, tracking its changes over time, and comparing it to the productivity of other resources. To demonstrate the applicability of our framework, we apply it to analyze employee behavior in an Australian company and evaluate its usefulness by a survey among industry managers.},
author = {Pika, Anastasiia and Leyer, Michael and Wynn, Moe T. and Fidge, Colin J. and Hofstede, Arthur H. M. Ter and Aalst, Wil M. P. Van Der},
doi = {10.1145/3041218},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Mining resource profiles from event logs .pdf:pdf},
isbn = {2158656X (ISSN)},
issn = {2158656X},
journal = {ACM Transactions on Management Information Systems},
number = {1},
pages = {1--30},
title = {{Mining Resource Profiles from Event Logs}},
url = {http://dl.acm.org/citation.cfm?doid=3068852.3041218},
volume = {8},
year = {2017}
}
@article{Suriadi2015,
abstract = {Through the application of process mining, valuable evidence-based insights can be obtained about business processes in organisations. As a result, the field has seen an increased uptake in recent years as evidenced by success stories and increased tool support. However, despite this impact, current performance analysis capabilities remain somewhat limited in the context of information-poor event logs. For example, natural daily and weekly patterns are not considered but they are vital for understanding the performance of processes and resources. In this paper, a new framework for analysing event logs is defined. Our framework is based on the concept of event interval. The framework allows for a systematic approach to sophisticated performance-related analysis beyond the capabilities of existing log-based analysis techniques, even with information-poor event logs. The paper formalises a range of event interval types and then presents an implementation as well as an evaluation of the proposed approach.},
author = {Suriadi, Suriadi and Ouyang, Chun and {Van Der Aalst}, Wil M.P. and {Ter Hofstede}, Arthur H.M.},
doi = {10.1016/j.dss.2015.07.007},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Event interval analysis -  Why do processes take time.pdf:pdf},
isbn = {0167-9236},
issn = {01679236},
journal = {Decision Support Systems},
keywords = {Business process management,Data mining,ProM,Process mining},
pages = {77--98},
publisher = {Elsevier B.V.},
title = {{Event interval analysis: Why do processes take time?}},
url = {http://dx.doi.org/10.1016/j.dss.2015.07.007},
volume = {79},
year = {2015}
}
@article{This2017b,
author = {This, Disclaimer},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/20170321{\_}Bui{\_}2016{\_}bb844270.pdf:pdf},
title = {{Eindhoven University of Technology MASTER An approach to select redesign best practices Bui, T.D.T.}},
year = {2017}
}
@book{Hutchison2014,
author = {Hutchison, David},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Mining Resource Scheduling Protocols.pdf:pdf},
isbn = {9783319101712},
title = {{LNCS 8659 - Business Process Management}},
year = {2014}
}
@article{Maliepaard2017,
author = {Maliepaard, Bas},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/Thesis{\_}Bas{\_}Maliepaard{\_}public{\_}version{\_}2017-09-08.pdf:pdf},
keywords = {keyword1, keyword2, keyword3},
number = {August},
title = {{Comparative analysis of standard and process}},
year = {2017}
}
@article{This2017c,
author = {This, Disclaimer},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Thesis/thesis{\_}Gietema{\_}public.pdf:pdf},
title = {{exact Creating an Operational Process Mining Methodology Based on a Single Case Study in Exact BSc . Technology Management Master of Science In Business Information Systems}},
year = {2017}
}
@article{Huang2012,
abstract = {Efficient resource behavior measure in business process management is a real and challenging problem. It reflects the actual situations in business process execution from resource perspective and is highly relevant for the business process performance. This paper presents an approach of measuring resource behavior from four important perspectives, i.e.; preference, availability, competence and cooperation, based on process mining. Furthermore, this paper shows how business process management can benefit from resource behavior measure. In particular, four applications are addressed to demonstrate the applicability of resource behavior measure in business process management. The presented approach is evaluated based on a proof-of-concept implementation and its application to a real case form health-care. The results show that the proposed approach is possible to improve current state of business process management. {\textcopyright} 2011 Elsevier Ltd. All rights reserved.},
author = {Huang, Zhengxing and Lu, Xudong and Duan, Huilong},
doi = {10.1016/j.eswa.2011.12.061},
file = {:C$\backslash$:/Users/mike/OneDrive/Prive/School/Thesis/Papers/Resource behavior measure and application in business process management.pdf:pdf},
isbn = {0957-4174},
issn = {09574174},
journal = {Expert Systems with Applications},
keywords = {Availability,Business process management,Competence,Cooperation,Preference,Resource behavior},
number = {7},
pages = {6458--6468},
publisher = {Elsevier Ltd},
title = {{Resource behavior measure and application in business process management}},
url = {http://dx.doi.org/10.1016/j.eswa.2011.12.061},
volume = {39},
year = {2012}
}
@article{aalst2013business,
  title={Business process management: a comprehensive survey},
  author={Van der Aalst, Wil MP},
  journal={ISRN Software Engineering},
  volume={2013},
  year={2013},
  publisher={Hindawi Publishing Corporation}
}

@inproceedings{aalst2011process,
  title={Process mining manifesto},
  author={Van Der Aalst, Wil and Adriansyah, Arya and De Medeiros, Ana Karla Alves and Arcieri, Franco and Baier, Thomas and Blickle, Tobias and Bose, Jagadeesh Chandra and van den Brand, Peter and Brandtjen, Ronald and Buijs, Joos and others},
  booktitle={International Conference on Business Process Management},
  pages={169--194},
  year={2011},
  organization={Springer}
}

@inproceedings{socialmining,
  title={Mining social networks: Uncovering interaction patterns in business processes},
  author={Van der Aalst, Wil MP and Song, Minseok},
  booktitle={International conference on business process management},
  pages={244--260},
  year={2004},
  organization={Springer}
}

@incollection{discovery,
  title={Process Discovery: An Introduction},
  author={Van der Aalst, Wil MP},
  booktitle={Process Mining},
  pages={125--156},
  year={2011},
  publisher={Springer}
}

@article{van2007business,
  title={Business process mining: An industrial application},
  author={van der Aalst, Wil MP and Reijers, Hajo A and Weijters, Anton JMM and van Dongen, Boudewijn F and De Medeiros, AK Alves and Song, Minseok and Verbeek, HMW},
  journal={Information Systems},
  volume={32},
  number={5},
  pages={713--732},
  year={2007},
  publisher={Elsevier}
}

@article{aalst2012process,
  title={Process mining put into context},
  author={Van Der Aalst, Wil MP and Dustdar, Schahram},
  journal={IEEE Internet Computing},
  volume={16},
  number={1},
  pages={82--86},
  year={2012},
  publisher={IEEE}
}

@article{kaufinann2006data,
  title={Data Mining: Concepts and Techniques},
  journal={Mining, What Is Data},
  author={Morgan Kaufinann},
  year={2006}
}

@book{aalst2016process,
  title={Process mining: data science in action},
  author={Van der Aalst, Wil MP},
  year={2016},
  publisher={Springer}
}

@inproceedings{arias2015framework,
  title={A framework for recommending resource allocation based on process mining},
  author={Arias, Michael and Rojas, Eric and Munoz-Gama, Jorge and Sep{\'u}lveda, Marcos},
  booktitle={International Conference on Business Process Management},
  pages={458--470},
  year={2015},
  organization={Springer}
}

@book{wolff1989stochastic,
  title={Stochastic modeling and the theory of queues},
  author={Wolff, Ronald W},
  year={1989},
  publisher={Pearson College Division}
}

@book{gross2008fundamentals,
  title={Fundamentals of queueing theory},
  author={Gross, Donald},
  year={2008},
  publisher={John Wiley \& Sons}
}

@inproceedings{rogge2016log,
  title={In log and model we trust?},
  author={Rogge-Solti, Andreas and Senderovich, Arik and Weidlich, Matthias and Mendling, Jan and Gal, Avigdor},
  booktitle={EMISA},
  pages={91--94},
  year={2016}
}

@article{thevendran2004perception,
  title={Perception of human risk factors in construction projects: an exploratory study},
  author={Thevendran, Vicknayson and Mawdesley, MJ},
  journal={International Journal of Project Management},
  volume={22},
  number={2},
  pages={131--137},
  year={2004},
  publisher={Elsevier}
}

@article{baccarini2004management,
  title={Management of risks in information technology projects},
  author={Baccarini, David and Salm, Geoff and Love, Peter ED},
  journal={Industrial Management \& Data Systems},
  volume={104},
  number={4},
  pages={286--295},
  year={2004},
  publisher={Emerald Group Publishing Limited}
}

@article{aalst2012replaying,
  title={Replaying history on process models for conformance checking and performance analysis},
  author={Van der Aalst, Wil and Adriansyah, Arya and van Dongen, Boudewijn},
  journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
  volume={2},
  number={2},
  pages={182--192},
  year={2012},
  publisher={Wiley Online Library}
}

@inproceedings{aalst2009performancevis,
  title={Process mining: fuzzy clustering and performance visualization},
  author={van Dongen, Boudewijn F and Adriansyah, Arya},
  booktitle={International Conference on Business Process Management},
  pages={158--169},
  year={2009},
  organization={Springer}
}

@article{hornix2007performance,
  title={Performance analysis of business processes through process mining},
  author={Hornix, Peter TG},
  journal={Master's Thesis, Eindhoven University of Technology},
  year={2007}
}