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Bibliography.bib
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@MastersThesis{Thoma:2017,
Title = {Analysis and Optimization of Convolutional Neural Network
Architectures},
Author = {Martin Thoma},
School = {Karlsruhe Institute of Technology},
Year = {2017},
Address = {Karlsruhe, Germany},
Month = jun,
Type = {Masters’s Thesis},
Keywords = {machine learning; artificial neural networks;
classification; supervised learning; CNNs},
Url = {https://martin-thoma.com/msthesis/}
}
@article{russell1995modern,
title={A modern approach},
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publisher={Citeseer}
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title={Dropout: a simple way to prevent neural networks from overfitting},
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journal={The Journal of Machine Learning Research},
volume={15},
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pages={1929--1958},
year={2014},
publisher={JMLR. org}
}
@inproceedings{glorot2011deep,
title={Deep sparse rectifier neural networks},
author={Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua},
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pages={315--323},
year={2011}
}
@article{bose2011analysis,
title={Analysis of Patient Treatment Procedures: The BPI Challenge Case Study},
author={Bose, RP Jagadeesh Chandra and van der Aalst, WMP},
year={2011},
publisher={BPMcenter. org}
}
@article{sivaganesan1994predictive,
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@incollection{weske2012business,
title={Business process management architectures},
author={Weske, Mathias},
booktitle={Business Process Management},
pages={333--371},
year={2012},
publisher={Springer}
}
@inproceedings{adriansyah2012mining,
title={Mining process performance from event logs: The bpi challenge 2012},
author={Adriansyah, A and Buijs, JCA M},
booktitle={Case Study. BPM Center Report BPM-12-15, BPMcenter. org},
year={2012},
organization={Citeseer}
}
@inproceedings{evermann2016,
title={A Deep Learning Approach for Predicting Process Behaviour at Runtime},
author={Joerg Evermann and Jana-Rebecca Rehse and Peter Fettke},
booktitle={Business Process Management Workshops},
year={2016}
}
@article{francescomarino2015,
title={Clustering-Based Predictive Process Monitoring},
author={Chiara Di Francescomarino and Marlon Dumas and Fabrizio Maria Maggi and Irene Teinemaa},
journal={CoRR},
year={2015},
volume={abs/1506.01428}
}
@article{francescomarino2018,
author = {Chiara Di Francescomarino and
Chiara Ghidini and
Fabrizio Maria Maggi and
Fredrik Milani},
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journal = {CoRR},
year = {2018}
}
@inproceedings{francescomarino2017,
title={An eye into the future: leveraging a-priori knowledge in predictive business process monitoring},
author={Di Francescomarino, Chiara and Ghidini, Chiara and Maggi, Fabrizio Maria and Petrucci, Giulio and Yeshchenko, Anton},
booktitle={International Conference on Business Process Management},
pages={252--268},
year={2017},
organization={Springer}
}
@book{kuhn2013applied,
title={Applied predictive modeling},
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volume={26},
year={2013},
publisher={Springer}
}
@misc{trevor2009elements,
title={The elements of statistical learning: data mining, inference, and prediction},
author={Trevor, Hastie and Robert, Tibshirani and JH, Friedman},
year={2009},
publisher={New York, NY: Springer}
}
@book{Aalst2016,
abstract = {This is the second edition of Wil van der Aalst's seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges.},
added-at = {2018-04-28T19:49:57.000+0200},
address = {Heidelberg},
author = {van der Aalst, Wil M. P.},
biburl = {https://www.bibsonomy.org/bibtex/29abf3ba029153a0cdd4eb81ac72c44d3/flint63},
description = {1. Auflage 2011},
doi = {10.1007/978-3-662-49851-4},
edition = 2,
file = {eBook:2016/Aalst16.pdf:PDF;Springer Pro:http\://www.springerprofessional.de/process-mining/10034662:URL;Springer Product page:http\://www.springer.com/978-3-662-49850-7:URL;Amazon Search inside:http\://www.amazon.de/gp/reader/3662498502/:URL;Related Web site:http\://www.processmining.org/:URL},
groups = {public},
interhash = {696cd8832fd7ffeabf4bde7abaa2d1b8},
intrahash = {9abf3ba029153a0cdd4eb81ac72c44d3},
isbn = {978-3-662-49850-7},
keywords = {01821 103 springer book shelf ai data processing pattern recognition business process workflow analysis},
publisher = {Springer},
timestamp = {2018-04-28T19:49:57.000+0200},
title = {Process Mining: Data Science in Action},
username = {flint63},
year = 2016
}
@article{drucker1999,
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}
@article{leclair2009,
title={Dynamic Case Management — An Old Idea Catches New Fire},
institution={Forrester},
author={LeClair, Craig and Moore, Connie},
year={2009}, month={Dec}
}
@article{hauder2014,
title={Research Challenges in Adaptive Case Management: A Literature Review},
author={Matheus Hauder and Simon Pigat and Florian Matthes},
journal={2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations},
year={2014},
pages={98-107}
}
@inproceedings{huber2015,
author = {Huber, Sebastian and Fietta, Marian and Hof, Sebastian},
title = {Next Step Recommendation and Prediction Based on Process Mining in Adaptive Case Management},
booktitle = {Proceedings of the 7th International Conference on Subject-Oriented Business Process Management},
series = {S-BPM ONE '15},
year = {2015},
isbn = {978-1-4503-3312-2},
location = {Kiel, Germany},
pages = {3:1--3:9},
articleno = {3},
numpages = {9},
url = {http://doi.acm.org/10.1145/2723839.2723842},
doi = {10.1145/2723839.2723842},
acmid = {2723842},
publisher = {ACM},
keywords = {adaptive case management, business process management, decision support, process mining, recommender systems},
}
@inproceedings{jozefowicz2015empirical,
title={An empirical exploration of recurrent network architectures},
author={Jozefowicz, Rafal and Zaremba, Wojciech and Sutskever, Ilya},
booktitle={International Conference on Machine Learning},
pages={2342--2350},
year={2015}
}
@misc{BPIC2011,
title = {BPI Challenge 2011 Dataset},
howpublished = {\url{https://data.4tu.nl/repository/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54}},
note = {Accessed: 2018-08-20}
}
@misc{BPIC2012,
title = {BPI Challenge 2012 Dataset},
howpublished = {\url{https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f}},
note = {Accessed: 2018-08-15}
}
@misc{BPIC2015,
title = {BPI Challenge 2015 Dataset},
howpublished = {\url{https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1}},
note = {Accessed: 2018-12-20}
}
@misc{BPIC2017,
title = {BPI Challenge 2017 Dataset},
howpublished = {\url{https://data.4tu.nl/repository/uuid:7e326e7e-8b93-4701-8860-71213edf0fbe}},
note = {Accessed: 2018-08-16}
}
@misc{Helpdesk,
title = {Helpdesk Dataset},
howpublished = {\url{http://dx.doi.org/10.17632/39bp3vv62t.1}},
note = {Accessed: 2018-12-22}
}
@misc{BPIC2013,
title = {BPI Challenge 2013 Dataset},
howpublished = {\url{https://data.4tu.nl/repository/uuid:7e326e7e-8b93-4701-8860-71213edf0fbe}},
note = {Accessed: 2018-12-22}
}
@misc{EnvLog,
title = {Environmental permit application process (“wabo”), coselog project - municipality 4},
howpublished = {\url{https://doi.org/10.4121/uuid: e8c3a53d-5301-4afb-9bcd-38e74171ca32}},
note = {Accessed: 2018-12-22}
}
@article{van2015benchmarking,
title={Benchmarking of five dutch municipalities with process mining techniques reveals opportunities for improvement},
author={van der Ham, Ube},
journal={Business Process Intelligence Challenge},
volume={2015},
year={2015}
}
@ARTICLE{metzger2015,
author={A. Metzger and P. Leitner and D. Ivanović and E. Schmieders and R. Franklin and M. Carro and S. Dustdar and K. Pohl},
journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
title={Comparing and Combining Predictive Business Process Monitoring Techniques},
year={2015},
volume={45},
number={2},
pages={276-290},
keywords={predictive business process monitoring techniques;process execution;machine learning;constraint satisfaction;QoS aggregation;quality-of-service aggregation;accuracy indicators;Business;Monitoring;Quality of service;Accuracy;Real-time systems;Data models;Predictive models;Business data processing;failure analysis;forecasting;neural network applications;transportation;Business data processing;failure analysis;forecasting;neural network applications;transportation},
doi={10.1109/TSMC.2014.2347265},
ISSN={2168-2216}}
@article{polato2014,
title={Data-aware remaining time prediction of business process instances},
author={Mirko Polato and Alessandro Sperduti and Andrea Burattin and Massimiliano de Leoni},
journal={2014 International Joint Conference on Neural Networks (IJCNN)},
year={2014},
pages={816-823}
}
@inproceedings{tax2017,
title={Predictive Business Process Monitoring with LSTM Neural Networks},
author={Niek Tax and Ilya Verenich and Marcello La Rosa and Marlon Dumas},
booktitle={CAiSE},
year={2017}
}
@article{tax2018interdisciplinary,
title={An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction},
author={Tax, Niek and Teinemaa, Irene and van Zelst, Sebastiaan J},
journal={arXiv preprint arXiv:1811.00062},
year={2018}
}
@inproceedings{keskar2016large,
title={On large-batch training for deep learning: Generalization gap and sharp minima},
author={Keskar, Nitish Shirish and Mudigere, Dheevatsa and Nocedal, Jorge and Smelyanskiy, Mikhail and Tang, Ping Tak Peter},
booktitle={ICLR},
year={2017}
}
@incollection{schoenig2018,
booktitle = {Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering},
title = {Deep Learning Process Prediction with Discrete and Continuous Data Features},
author = {Stefan Sch{\"o}nig and Richard Jasinski and Lars Ackermann and Stefan Jablonski},
address = {s.l.},
year = {2018},
url = {https://eref.uni-bayreuth.de/41777/}
}
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title={Framewise phoneme classification with bidirectional LSTM networks},
author={Alex Graves and Juergen Schmidhuber},
journal={Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.},
year={2005},
volume={4},
pages={2047-2052 vol. 4}
}
@article{schmidhuber2015deep,
title={Deep learning in neural networks: An overview},
author={Schmidhuber, J{\"u}rgen},
journal={Neural networks},
volume={61},
pages={85--117},
year={2015},
publisher={Elsevier}
}
@inproceedings{lipton2018,
author = {{Lipton}, Z.~C. and {Steinhardt}, J.},
title = {Troubling Trends in Machine Learning Scholarship},
booktitle = {ICML},
year = {2018}
}
@inproceedings{rogge2013,
title={Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays},
author={Andreas Rogge-Solti and Mathias Weske},
booktitle={ICSOC},
year={2013}
}
@misc{lessmannBADS,
author = {Stefan Lessmann},
title = {Business Analytics \& Data Science lecture},
year = {Winter term 2015/2016},
publisher = {Humboldt-Universität zu Berlin}
}
@article{hochreiter1997,
author = {Hochreiter, Sepp and Schmidhuber, J\"{u}rgen},
title = {Long Short-Term Memory},
journal = {Neural Comput.},
issue_date = {November 15, 1997},
volume = {9},
number = {8},
month = nov,
year = {1997},
issn = {0899-7667},
pages = {1735--1780},
numpages = {46},
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doi = {10.1162/neco.1997.9.8.1735},
acmid = {1246450},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
}
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author = {F. Rosenblatt},
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journal = {Psychological Review},
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}
@inproceedings{tsoumakas2009,
title={An Ensemble Pruning Primer},
author={Grigorios Tsoumakas and Ioannis Partalas and Ioannis P. Vlahavas},
booktitle={Applications of Supervised and Unsupervised Ensemble Methods},
year={2009}
}
@online{web:taylorism-and-drucker,
Title = {Case Management For The Knowledge Worker Era},
Url = {https://jvzoggel.com/2016/08/16/case-management-for-the-knowledge-worker-era/},
Urldate = {2018-12-28}
}
@online{web:keras-lstm-state,
Title = {When does Keras reset an LSTM state?},
Url = {https://stackoverflow.com/questions/43882796/when-does-keras-reset-an-lstm-state},
Urldate = {2018-12-7}
}
@online{web:stackoverflow-keras-class-weights,
Title = {How to set weights for imbalanced classes in Keras},
Url = {https://datascience.stackexchange.com/questions/13490/how-to-set-class-weights-for-imbalanced-classes-in-keras},
Urldate = {2018-12-27}
}
@online{web:tensorflow,
Title = {Tensorflow},
Url = {https://www.tensorflow.org/},
Urldate = {2018-09-22}
}
@online{web:economist:jit,
Title = {Just in Time - Idea},
Url = {https://www.economist.com/news/2009/07/06/just-in-time},
Urldate = {2018-12-6}
}
@online{web:pandas-get-dummies,
Title = {Pandas get dummies function documentation},
Url = {https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html},
Urldate = {2018-12-27}
}
@online{web:lstm-effectiveness,
Title = {The Unreasonable Effectiveness Of Recurrent Neural Networks},
Url = {https://karpathy.github.io/2015/05/21/rnn-effectiveness/},
Urldate = {2019-01-07}
}
@online{web:spice,
Title = {Sequence Prediction Challenge (SPICE)},
Url = {http://spice.lif.univ-mrs.fr/},
Urldate = {2018-09-22}
}
@online{web:weka,
Title = {Waikato Environment for Knowledge Analysis (Weka)},
Url = {https://www.cs.waikato.ac.nz/~ml/weka/},
Urldate = {2018-10-13}
}
@online{web:cmmn,
Title = {Case Management Modeling Notation},
Url = {https://www.omg.org/cmmn/},
Urldate = {2018-09-27}
}
@online{web:ahogrammer,
Title = {Ahogrammer | List of pretrained word embeddings},
Url = {http://ahogrammer.com/2017/01/20/the-list-of-pretrained-word-embeddings/},
Urldate = {2018-10-11}
}
@online{web:colah,
Title = {Understanding LSTM Networks -- colah's blog},
Url = {https://colah.github.io/posts/2015-08-Understanding-LSTMs/},
Urldate = {2018-10-12}
}
@online{web:evermann,
Title = {Joerg Evermann - Software},
Url = {https://joerg.evermann.ca/software.html},
Urldate = {2018-11-09}
}
@online{web:text-generation-machinelearningmastery,
Title = {Text Generation With LSTM Recurrent Neural Networks},
Url = {https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/},
Urldate = {2018-11-08}
}
@online{web:text-generation-freecodecamp,
Title = {An applied introduction for LSTMs for text generation},
Url = {https://medium.freecodecamp.org/applied-introduction-to-lstms-for-text-generation-380158b29fb3},
Urldate = {2018-11-08}
}
@online{web:word-embedding,
Title = {Introduction to word embedding and Word2Vec},
Url = {https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa},
Urldate = {2018-11-08}
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