Title
Toward Dynamic Model Association through Semantic Analytics: Approach and Evaluation
Abstract
Business Architecture (BA) is often used as a blueprint that aligns an enterprise's capabilities and processes with its strategic objectives and structures. Conventionally, the alignment is established manually by subject matter experts through the association between model elements based on their domain knowledge; while the quality of the alignment is high, the effort is costly and time-consuming. To overcome these issues, we propose a novel yet complementary approach wherein associations between model elements are automatically and dynamically established and maintained through semantic analytics, such as natural language understanding and synonym tag generation. In this paper, we describe how the dynamic model association is implemented in IBM's CBM.next project, evaluate initial results from the deployment, and discuss directions for future research.
Year
DOI
Venue
2019
10.1109/CBI.2019.00022
2019 IEEE 21st Conference on Business Informatics (CBI)
Keywords
DocType
Volume
Business Architecture,Semantic Analytics,Keyword Extraction,Data Indexing and Search,Dynamic Model Association
Conference
01
ISSN
ISBN
Citations 
2378-1963
978-1-7281-0651-9
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Lei Huang15928.22
Guang-Jie Ren214413.08
Shun Jiang342.93
Eric Young Liu400.68