000			04357nam a22002537a 4500
008			210318b2021 a|||f mb|| 00| 0 eng d
040			_aEG-CaNU _cEG-CaNU
041	0		_aeng _beng
082			_a610
100	0		_aFadi Nader Zaki Baskharon _9498
245	1		_aPredicting Remaining Cycle Time from Ongoing Cases: _bA Survival Analysis-Based Approach / _cFadi Nader Zaki Baskharon
260			_c2021
300			_a 51 p. _bill. _c21 cm.
500			_3Supervisor: Mohamed A. ElHelw
502			_aThesis (M.A.)—Nile University, Egypt, 2021 .
504			_a"Includes bibliographical references"
505	0		_aContents: 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Thesis Outline and Summary of Contributions . . . . . . . . . . . . . . . . . . 4 2. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Survival Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.3 Weibull distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.4 Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.3 Baseline Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3. Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.1 Model Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Optimization function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Output interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.1 Experiment I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2 Experiment II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 vii 5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Appendices: A. cycle prediction documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Bibliography .
520	3		_aAbstract: in predictive process monitoring. Different approaches that learn from event logs, e.g., relying on an existing representation of the process or leveraging machine learning approaches, have been proposed in the literature to tackle this problem. Machine learning-based techniques have shown superiority over other techniques with respect to the accuracy of the prediction as well as freedom from knowledge about the underlying process models generating the logs. However, all proposed approaches only learn from complete traces. This might cause delays in starting new training cycles as usually process instances last over a long time that could even reach months or years. In this thesis, we propose a machine learning approach that can also accept and learn from incomplete (ongoing) traces. Using a time-aware survival analysis technique, we can train a neural network to predict the most likely remaining cycle time of a running case. This approach is evaluated on different real-life datasets and is compared with a state-ofthe- art baseline. Results show that our approach, in most cases, is able to outperform the baseline approach with a simple model architecture and less training time. The approach is further enhanced to learn from trace level - fixed - features as well as the events-related features. We empirically proved that trace-level features enhance the prediction power of the model using a real-life dataset.
546			_aText in English, abstracts in English.
650		4	_aInformatics
655		7	_2NULIB _aDissertation, Academic _9187
690			_aInformatics _9499
942			_2ddc _cTH
999			_c9034 _d9034