Predictive End-to-End Enterprise Process Network Monitoring

被引：5

作者：

Oberdorf, Felix ^{[1
]}

Schaschek, Myriam ^{[2
]}

Weinzierl, Sven ^{[3
]}

Stein, Nikolai ^{[1
]}

Matzner, Martin ^{[3
]}

Flath, Christoph M. ^{[1
]}

机构：

[1] Julius Maximilians Univ Wurzburg, Chair Informat Syst & Business Analyt, Sanderring 2, D-97070 Wurzburg, Germany

[2] Julius Maximilians Univ Wurzburg, Chair Business Management & Informat Syst, Sanderring 2, D-97070 Wurzburg, Germany

[3] Friedrich Alexander Univ Erlangen Nurnberg, Chair Digital Ind Serv Syst, Further Str 248, D-90429 Nurnberg, Germany

来源：

BUSINESS & INFORMATION SYSTEMS ENGINEERING | 2023年 / 65卷 / 01期

关键词：

Predictive process analytics; Predictive process monitoring; Deep learning; Machine learning; Neural network; Business process anagement; Process mining; TIME PREDICTION; NEURAL-NETWORK; CONCEPT DRIFT; DRIVEN; MODELS;

D O I：

10.1007/s12599-022-00778-4

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method's utility for organizations.

引用

页码：49 / 64

页数：16

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