Autoencoders for improving quality of process event logs
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Marco Comuzzi | Jongchan Kim | Hoang Thi Cam Nguyen | Jonghyeon Ko | Suhwan Lee | Suhwan Lee | M. Comuzzi | Jongchan Kim | Jonghyeon Ko | Hoang Thi Cam Nguyen
[1] R. Cook. Detection of influential observation in linear regression , 2000 .
[2] Ricardo Seguel,et al. Process Mining Manifesto , 2011, Business Process Management Workshops.
[3] Mohsen Attaran,et al. Exploring the relationship between information technology and business process reengineering , 2004, Inf. Manag..
[4] Ahmed Awad,et al. Deducing Case IDs for Unlabeled Event Logs , 2015, Business Process Management Workshops.
[5] Marlon Dumas,et al. Predictive Business Process Monitoring with LSTM Neural Networks , 2016, CAiSE.
[6] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[7] H. Abdi,et al. Principal component analysis , 2010 .
[8] Jacques Wainer,et al. Algorithms for anomaly detection of traces in logs of process aware information systems , 2013, Inf. Syst..
[9] Wil M. P. van der Aalst,et al. Wanna improve process mining results? , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).
[10] Max Mühlhäuser,et al. Analyzing business process anomalies using autoencoders , 2018, Machine Learning.
[11] Luigi Pontieri,et al. Outlier Detection Techniques for Process Mining Applications , 2008, ISMIS.
[12] Moe Thandar Wynn,et al. Detection and Interactive Repair of Event Ordering Imperfection in Process Logs , 2018, CAiSE.
[13] Antonio Ruiz-Cortés,et al. Predictive Monitoring of Business Processes: A Survey , 2018, IEEE Transactions on Services Computing.
[14] Sander J. J. Leemans,et al. PM ^2 : A Process Mining Project Methodology , 2015, CAiSE.
[15] Pedro Rangel Henriques,et al. A Formal Definition of Data Quality Problems , 2005, ICIQ.
[16] Max Mühlhäuser,et al. Unsupervised Anomaly Detection in Noisy Business Process Event Logs Using Denoising Autoencoders , 2016, DS.
[17] Mathias Weske,et al. Repairing Event Logs Using Timed Process Models , 2013, OTM Workshops.
[18] Luciano Baresi,et al. Multi-party business process compliance monitoring through IoT-enabled artifacts , 2018, Inf. Syst..
[19] Carlo Batini,et al. Methodologies for data quality assessment and improvement , 2009, CSUR.
[20] Wil M. P. van der Aalst,et al. Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance , 2005, WISP@ICATPN.
[21] Erhard Rahm,et al. Data Cleaning: Problems and Current Approaches , 2000, IEEE Data Eng. Bull..
[22] Yoshua Bengio,et al. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.
[23] Moe Thandar Wynn,et al. Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs , 2017, Inf. Syst..
[24] Akhil Kumar,et al. Process mining on noisy logs - Can log sanitization help to improve performance? , 2015, Decis. Support Syst..
[25] Claudia Diamantini,et al. Discovering anomalous frequent patterns from partially ordered event logs , 2018, Journal of Intelligent Information Systems.
[26] Wil M. P. van der Aalst,et al. Process Mining in Healthcare: Data Challenges When Answering Frequently Posed Questions , 2012, ProHealth/KR4HC.
[27] J. Carpenter,et al. Practice of Epidemiology Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study , 2014 .
[28] Jianmin Wang,et al. Efficient Recovery of Missing Events , 2013, IEEE Transactions on Knowledge and Data Engineering.
[29] Nittaya Kerdprasop,et al. An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm , 2015 .
[30] Stefanie Rinderle-Ma,et al. Multi Instance Anomaly Detection in Business Process Executions , 2017, BPM.
[31] Jeffrey Pennington,et al. Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection , 2011, NIPS.
[32] et al.,et al. Missing Data Imputation in the Electronic Health Record Using Deeply Learned Autoencoders , 2017, PSB.
[33] Mathias Weske,et al. Improving Documentation by Repairing Event Logs , 2013, PoEM.
[34] Takehisa Yairi,et al. Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction , 2014, MLSDA'14.
[35] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.