A Evaluation Method for Web Service with Large Numbers of Historical Records

Qi, Lianyong; Ni, Jian; Xia, Xiaona; Yan, Chao; Wang, Hua; Huang, Wanli

doi:10.1109/trustcom.2014.94

Cited by 3 publications

(1 citation statement)

References 16 publications

(15 reference statements)

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“…However, Last-K only considers some ''new'' historical records, while overlooks some ''old'' but important ones. Similarly, a weight model Partial-HR is proposed in Qi et al, 17 where only the recent 20% historical records are employed for service evaluation. Liu et al 18 and Wu et al 19 adopt ''arithmetical progression'' manner and ''geometric progression'' manner, respectively, for weight assignment of historical records.…”

Section: Related Work and Comparison Analysesmentioning

confidence: 99%

“Time–Location–Frequency”–aware Internet of things service selection based on historical records

Dai

et al. 2017

International Journal of Distributed Sensor Networks

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The advertised quality of an Internet of things service is not always trustable due to the exaggerated quality propagation and dynamic network environment. Therefore, it is more trustable to evaluate the Internet of things service quality based on the historical execution records of service. However, an Internet of things service often has multiple historical records whose invocation time and location are different, which makes it necessary to weigh each historical record of an identical Internet of things service. Besides, for different candidate Internet of things services, their invocation frequencies are often varied, which may also affect the final service selection decision of target user. In view of the above two challenges, a novel service selection approach ''Time-Location-Frequency''-aware Service Selection Approach is put forward in this article. In Time-Location-Frequency-aware Service Selection Approach, we first weigh each historical record of an Internet of things service, based on its service invocation time and location; afterward, we weigh each candidate Internet of things service based on its invocation frequency; finally, with the derived two kinds of weights, we evaluate each candidate Internet of things service and return the quality-optimal one to the target user. At last, through a set of experiments deployed on a real service quality data set WS-DREAM, we validate the feasibility of our proposal.

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Section: Related Work and Comparison Analysesmentioning

confidence: 99%