Recommending collaborations with newly emerged services for composition creation in cloud manufacturing

ABSTRACT

Cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm in which manufacturing resources and capabilities are published as services and collaborate with other services to form service compositions. New services are continuously emerging, promoting the development of CMfg services systems. The providers of newly emerged services naturally expect their services to be invoked soon, while composition developers are curious about and unfamiliar with the usage scenarios of newly emerged services. For better leveraging newly emerged services and facilitating the related composition creation, a Service Collaboration Recommendation (SCR) framework is proposed to recommend collaborations with newly emerged services based on the service functionality. As both fine-grained and coarse-grained functionality features, as well as historical records of invocations and collaborations, are well utilized, the time-adaptive framework generates recommendations of collaborations, including that among dissimilar or newly emerged services. In this way, the usage scenarios of newly emerged services can be proactively illustrated to both service providers and composition developers. Comprehensive experiments with multiple recommending frequency settings over a real-world dataset demonstrate that SCR achieves better performance in collaboration recommendations than baseline methods.

KEYWORDS:

• Cloud manufacturing
• newly emerged service
• service collaboration
• service recommendation
• service composition

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://www.programmableweb.com/.

Additional information

Funding

This research has been partially supported by the National Key Research and Development Program of China under Grant [No.2018YFB1402500]; the National Natural Science Foundation of China under Grant [No.61673230]; and the High-Tech Ship Research Project of China under Grant [No.17GC26102.01]. Yushun Fan is the corresponding author.