2016 IEEE International Conference on Web Services (ICWS)最新文献

PLMwsp: Probabilistic Latent Model for Web Service QoS Prediction Web服务QoS预测的概率潜在模型

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-09-01 DOI: 10.1109/ICWS.2016.86

Bobaker Mohamed A. Madi, Quan Z. Sheng, Lina Yao, Yongrui Qin, Xianzhi Wang

引用次数: 7

Optimized Composite Service Transactions through Execution Results Prediction 通过执行结果预测优化组合服务事务

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-09-01 DOI: 10.1109/ICWS.2016.107

Jiuyun Xu, Zhaotong Li, Huanxing Chi, Muhan Wang, Chao Guan, S. Reiff-Marganiec, Huilin Shen

引用次数: 4

Scheduling Stochastic Multi-stage Jobs on Elastic Computing Services in Hybrid Clouds 混合云弹性计算服务下的随机多阶段作业调度

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.94

Jie Zhu, Xiaoping Li, Rubén Ruiz, Xiaolong Xu, Yi Zhang

引用次数: 11

Time-Aware Collaborative Poisson Factorization for Service Recommendation 服务推荐的时间感知协同泊松分解

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.33

Shuhui Chen, Yushun Fan, Wei Tan, Jia Zhang, Bing Bai, Zhenfeng Gao

{"title":"Time-Aware Collaborative Poisson Factorization for Service Recommendation","authors":"Shuhui Chen, Yushun Fan, Wei Tan, Jia Zhang, Bing Bai, Zhenfeng Gao","doi":"10.1109/ICWS.2016.33","DOIUrl":"https://doi.org/10.1109/ICWS.2016.33","url":null,"abstract":"With the booming number of web services, it is a challenge for inexperienced developers to select suitable services and make service compositions. Therefore, recommending services based on user queries becomes a necessity. For modeling the queries and services' descriptions, many recent studies are based on LDA (Latent Dirichlet Allocation). However, some previous empirical works indicate that LDA model doesn't gain high accuracy in generating latent presentation which is subject to the restrictive assumption of the Dirichlet-Multinomial distribution. In this paper, we propose a Time-aware Collaborative Poisson Factorization (TCPF) to tackle the problem. TCPF takes Poisson Factorization as the foundation to model mashup queries and service descriptions separately, and incorporate them with the historical usage data together using collective matrix factorization. Experiments on the real-world ProgrammableWeb dataset show that our model outperforms the state-of-the-art methods (e.g., Time-aware collaborative domain regression) by 7.7% in terms of mean average precision, and costs much less time on the sparse, massive and long-tailed data set.","PeriodicalId":271249,"journal":{"name":"2016 IEEE International Conference on Web Services (ICWS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124804568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Layer-Hierarchical Scientific Workflow Recommendation 分层科学工作流程推荐

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.97

Zehui Cheng, Zhangbing Zhou, P. Hung, Ke Ning, Liang-Jie Zhang

引用次数: 4

A Temporal-Aware Hybrid Collaborative Recommendation Method for Cloud Service 一种时间感知的云服务混合协同推荐方法

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.40

Shunmei Meng, Zuojian Zhou, T. Huang, Duanchao Li, Song Wang, Fan Fei, Wenping Wang, Wanchun Dou

{"title":"A Temporal-Aware Hybrid Collaborative Recommendation Method for Cloud Service","authors":"Shunmei Meng, Zuojian Zhou, T. Huang, Duanchao Li, Song Wang, Fan Fei, Wenping Wang, Wanchun Dou","doi":"10.1109/ICWS.2016.40","DOIUrl":"https://doi.org/10.1109/ICWS.2016.40","url":null,"abstract":"With the rapid development of cloud computing, large scale of cloud services are provided to users. Recommender systems have been proven to be valuable tools to deal with information overload and be able to provide appropriate recommendations to users. The cloud environment is dynamic and uncertain, which makes the quality of cloud services time-sensitive. However, most existing recommender systems did not take temporal influence into consideration, therefore could not accommodate the dynamic cloud environment. In view of this challenge, we propose a temporal-aware hybrid collaborative recommendation method for cloud service. It aims at providing users with appropriate recommendations from time-sensitive cloud services. In our method, by distinguishing temporal QoS metrics from stable QoS metrics, temporal influence is integrated into classical neighborhood-based collaborative recommender algorithm. Besides, to get an optimal recommendation, a temporal-aware latent factor model based on tensor decomposition is proposed and combined to improve the recommendation performance. Finally, experiments are designed and conducted to demonstrate the efficiency of our method.","PeriodicalId":271249,"journal":{"name":"2016 IEEE International Conference on Web Services (ICWS)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Integrating Theoretical Modeling and Experimental Measurement for Soft Resource Allocation in Multi-tier Web Systems 多层Web系统软资源分配的理论建模与实验测量集成

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.73

Yuliang Shi, Jiwei Huang, Xudong Zhao, Lei Liu, Shijun Liu, Li-zhen Cui

引用次数: 5

Asymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation 面向Web服务推荐的非对称相关正则矩阵分解

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.34

Qi Xie, Shenglin Zhao, Zibin Zheng, Jieming Zhu, Michael R. Lyu

{"title":"Asymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation","authors":"Qi Xie, Shenglin Zhao, Zibin Zheng, Jieming Zhu, Michael R. Lyu","doi":"10.1109/ICWS.2016.34","DOIUrl":"https://doi.org/10.1109/ICWS.2016.34","url":null,"abstract":"Web service recommendation has recently drawn much attention with the growing amount of Web services. Previous work usually exploits the collaborative filtering techniques for Web service recommendation, but suffers from the data sparsity problem that leads to inaccurate results. Our analysis on a real-world Quality of Service (QoS) dataset shows that there is a hidden correlation among users and services. We define such hidden correlation with an asymmetric matrix (namely asymmetric correlation), in which each entry presents the hidden correlation between a user pair or between a service pair. The goal of this work is to employ such asymmetric correlation among users and services to alleviate the data sparsity problem and further enhance the prediction accuracy in service recommendation. Specifically, we propose an asymmetric correlation regularized matrix factorization (MF) framework, in which asymmetric correlation and asymmetric correlation propagation have been naturally integrated. Finally, experimental results on a well-known real-world QoS dataset validate that the use of asymmetric correlation among users and services is effective in improving prediction accuracy for Web service recommendation.","PeriodicalId":271249,"journal":{"name":"2016 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116093346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Minimally Invasive Semantification of Light Weight Service Descriptions 轻量级服务描述的微创语义化

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.93

Fathoni A. Musyaffa, Lavdim Halilaj, R. Siebes, F. Orlandi, S. Auer

引用次数: 17

BPCS: A Block-Based Service Process Caching Strategy to Accelerate the Execution of Service Processes BPCS:一种基于块的加速服务进程执行的服务进程缓存策略

2016 IEEE International Conference on Web Services (ICWS) Pub Date : 2016-06-01 DOI: 10.1109/ICWS.2016.28

Tingjie Jia, Jian Cao, Yan Yao, Zitai Ma

引用次数: 1