Name
Title | ||
|---|---|---|
A gaussian fields based mining method for semi-automating staff assignment in workflow application |
Abstract | ||
|---|---|---|
Staff assignment is a very important task in the research of workflow resource management. Currently, many well-known workflow applications still rely on human resource assigners such as process initiator or process monitor to perform staff assignment task. In this paper, we propose a semi-automatic workflow staff assignment method which can decrease the workload of staff assigner based on a novel semi-supervised machine learning framework. Our method can be applied to learn all kinds of activities that each actor is capable of based on the workflow event log. After we have learned all labeled data, we can suggest a suitable actor to undertake the specified activities when a new process is assigned. With the proposed method, we can get an average prediction accuracy of 97% and 91% on the data sets of two manufacturing enterprise applications respectively. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2600821.2600843 | ICSSP |
Keywords | Field | DocType |
staff assignment,workflow,human factors,gaussian fields,resource management,management,theory,machine learning,learning | Resource management,Data set,Workflow technology,Software engineering,Computer science,Workload,Workflow application,Workflow engine,Workflow,Workflow management system,Database | Conference |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
5 | ||