移动应用中基于云模型的不确定服务Skyline查询*

Longchang Zhang, Gyan Amos, Jing Bai, Xinyue Zhang
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引用次数: 1

摘要

基于移动用户评分的QoS表现出模糊性和随机性,具有相同功能的服务数量较多,使得基于QoS的服务选择困难。针对上述问题,本文首次提出了基于移动用户评分的云模型来描述QoS。然后,为了缩小最优服务的搜索范围,提出了基于云模型的不确定服务Skyline计算方法,并设计了四种不确定服务Skyline查询算法(BNL_CM、D&C_CM、NN_CM和BBS_CM)。实例和实验表明:1)云模型在描述QoS方面比均值、区间数、模糊数和随机数具有更好的性能;2)不确定的服务天际线可以有效缩小服务搜索范围;3)与BNL_CM、D&C_CM和NN_CM相比,BBS_CM具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertain Service Skyline Queries Based on Cloud Model in Mobile Application*
The QoS based on mobile user ratings shows the fuzziness and randomness, and the number of services with the same function is larger, which makes service selection based on QoS hard. To solve above issue, this paper firstly proposes to use cloud model to describe QoS based on mobile user ratings. Then to reduce the search range for optimal service, this paper puts forward uncertain service Skyline calculation method based on cloud model and designs four kinds of uncertain service Skyline query algorithms (BNL_CM, D&C_CM, NN_CM and BBS_CM). Examples and experiments show that: 1) the cloud model has better performance in the description of QoS than the mean, interval number, fuzzy numbers, and random number; 2) Uncertain service Skyline can effectively reduce the service search range; 3) BBS_CM has better performance compared to BNL_CM, D&C_CM and NN_CM.
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