Paper Info
Title
Extracting Promising Topics On Smart Manufacturing Based On Latent Dirichlet Allocation (Lda)
Abstract
Although smart manufacturing (SM) has attracted enormous attention, it is ambiguous how to realize it due to lack of practical evidence and academic knowledge on technological components. Accordingly, it is required to explore knowledge landscape to investigate promising technologies. For this purpose, this study extracts 35 topics discussed in abstracts in previous literatures by employing Latent Dirichlet Allocation. The analysis results unveil big data, product information management, cyber-physical system, cloud manufacturing platform, and industrial Internet of things are identified as promising topics. It is also noteworthy that SM needs a unified vision because topics are diverged rather than converged.
Year
DOI
Venue
2019
10.1109/ictc46691.2019.8939701
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE
Keywords
Field
DocType
smart manufacturing, intelligent manufacturing, topics, Latent Dirichlet Allocation, LDA
Data science,Product information management,Cloud manufacturing,Smart manufacturing,Latent Dirichlet allocation,Computer science,Industrial Internet,Big data
Conference
ISSN
Citations 
PageRank 
2162-1233
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Young Seog Yoon100.34
Junhee Lee253.78
KwangRoh Park318628.70