Model-based decision support for knowledge-intensive processes

Author:

Seidel AnjoORCID,Haarmann Stephan,Weske Mathias

Abstract

AbstractProcess-aware information systems guide participants through the execution of processes. However, existing systems have limited support for knowledge-intensive processes, which are multi-variant and shaped by informed decisions of knowledge workers. Yet, making such decisions causes high cognitive load, as the effect of the decision on the future process execution must be considered. This may cause errors and/or slow down the process execution. We present an approach based on fragment-based Case Management. It supports the iterative decision making by (i) enabling knowledge workers to define goals and (ii) by giving recommendations on which decision outcomes align with the goals and which do not. For that, we use information from the process model and the running process instance. We show the technical feasibility with a proof-of-concept implementation and the value for knowledge workers in a preliminary user study.

Funder

Hasso-Plattner-Institut für Digital Engineering gGmbH

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Model-Based Recommendations for Next-Best Actions in Knowledge-Intensive Processes;Lecture Notes in Computer Science;2024

2. Editorial: recent advances in process analytics;Journal of Intelligent Information Systems;2023-07-06

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