Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring Article Swipe

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An Nguyen , Srijeet Chatterjee , Sven Weinzierl , Leo Schwinn , Martin Matzner , Bjoern M. Eskofier ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2010.00889

Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next activity and timestamp prediction can help to improve the performance of operational business processes. Recently, many PBPM solutions based on deep learning were proposed by researchers. Due to the sequential nature of event log data, a common choice is to apply recurrent neural networks with long short-term memory (LSTM) cells. We argue, that the elapsed time between events is informative. However, current PBPM techniques mainly use 'vanilla' LSTM cells and hand-crafted time-related control flow features. To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells. T-LSTM cells incorporate the elapsed time between consecutive events inherently to adjust the cell memory. Furthermore, we introduce cost-sensitive learning to account for the common class imbalance in event logs. Our experiments on publicly available benchmark event logs indicate the effectiveness of the introduced techniques.

Related Topics

Computer Science

Benchmark (Surveying)

Artificial Intelligence

Process (Computing)

Machine Learning

Recurrent Neural Network

Deep Learning

Long Short-Term Memory

Concepts

Computer science Timestamp Benchmark (surveying) Event (particle physics) Business process Artificial intelligence Process (computing) Machine learning Recurrent neural network Deep learning Long short term memory Class (philosophy) Process mining Artificial neural network Data mining Business process modeling Real-time computing Work in process Geography Marketing Operating system Geodesy Quantum mechanics Physics Business

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2010.00889
PDF: https://arxiv.org/pdf/2010.00889
OA Status: green
Cited By: 6
References: 18
Related Works: 10
OpenAlex ID: https://openalex.org/W3089641966

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DOI: https://doi.org/10.48550/arxiv.2010.00889

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Title: Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-10-02

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Authors: An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern M. Eskofier

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Landing page: https://arxiv.org/abs/2010.00889

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Concepts: Computer science, Timestamp, Benchmark (surveying), Event (particle physics), Business process, Artificial intelligence, Process (computing), Machine learning, Recurrent neural network, Deep learning, Long short term memory, Class (philosophy), Process mining, Artificial neural network, Data mining, Business process modeling, Real-time computing, Work in process, Geography, Marketing, Operating system, Geodesy, Quantum mechanics, Physics, Business

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Cited by: 6

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Citations by year (recent): 2024: 1, 2022: 1, 2021: 4

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