Nonnegative Matrix Factorization Based Heterogeneous Graph Embedding Method for Trigger-Action Programming in IoT

Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing m...

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Vydáno v:IEEE transactions on industrial informatics Ročník 18; číslo 2; s. 1231 - 1239
Hlavní autoři: Xing, Yongheng, Hu, Liang, Zhang, Xiaolu, Wu, Gang, Wang, Feng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Artificial intelligence
Cameras
Data mining
Embedding
Factorization
Feature extraction
Graph embedding
Heterogeneity
heterogeneous information networks
Internet of Things
Internet of Things (IoT)
Machine learning
nonnegative matrix factorization
Programming
Semantics
trigger-action programming (TAP)
Web services
ISSN:1551-3203, 1941-0050
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Shrnutí:Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To address this issue, in this article, we propose IoT nonnegative matrix factorization (IoT-NMF), a NMF-based heterogeneous graph embedding method for TAP. Prior to using IoT-NMF, we map triggers and actions to an IoT heterogeneous information network, from which we can extract three structures that preserve heterogeneous relations in triggers and actions. IoT-NMF can factorize the structures simultaneously for getting low-dimensional representation vectors of the triggers and actions, which can be further utilized in Artificial Intelligence of Things applications (e.g., TAP rule recommendation). Finally, we demonstrate the proposed approach using an if this then that (IFTTT) dataset. The result shows that IoT-NMF outperforms the state-of-the-art approaches.
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3092774