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2021 | OriginalPaper | Buchkapitel

DeepProcess: Supporting Business Process Execution Using a MANN-Based Recommender System

verfasst von : Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Hoa Dam, Renuka Sindhgatta

Erschienen in: Service-Oriented Computing

Verlag: Springer International Publishing

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Abstract

Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing a process-aware recommender system. We propose a novel network architecture, namely Write-Protected Dual Controller Memory-Augmented Neural Network(DCw-MANN), for building prescriptive models. To evaluate the feasibility and usefulness of our approach, we consider three real-world datasets and show that our approach leads to better performance on several baselines for the task of suffix recommendation and next task prediction.

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Vorheriges Kapitel Assessing the Impact of Context Data on Process Outcomes During Runtime

Nächstes Kapitel Interval-Based Remaining Time Prediction for Business Processes

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Titel: DeepProcess: Supporting Business Process Execution Using a MANN-Based Recommender System
verfasst von: Asjad Khan
Hung Le
Kien Do
Truyen Tran
Aditya Ghose
Hoa Dam
Renuka Sindhgatta
Verlag: Springer International Publishing
Buch: Service-Oriented Computing
Print ISBN: 978-3-030-91430-1

Electronic ISBN: 978-3-030-91431-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-91431-8_2

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