A Recommendation System to Facilitate Business Process Modeling
Document Type
Article
Publication Date
6-1-2017
Abstract
This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.
Identifier
84963636480 (Scopus)
Publication Title
IEEE Transactions on Cybernetics
External Full Text Location
https://doi.org/10.1109/TCYB.2016.2545688
ISSN
21682267
PubMed ID
27076482
First Page
1380
Last Page
1394
Issue
6
Volume
47
Recommended Citation
Deng, Shuiguang; Wang, Dongjing; Li, Ying; Cao, Bin; Yin, Jianwei; Wu, Zhaohui; and Zhou, Mengchu, "A Recommendation System to Facilitate Business Process Modeling" (2017). Faculty Publications. 9557.
https://digitalcommons.njit.edu/fac_pubs/9557