2020 IEEE International Conference on Web Services (ICWS)最新文献

On the Maintenance of a Scientific Application based on Microservices: an Experience Report 基于微服务的科学应用维护:经验报告

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00021

Leonardo P. Tizzei, L. Azevedo, E. Soares, R. Thiago, R. Costa

引用次数: 2

A Co-Attention Model with Sequential Behaviors and Side Information for Session- based Recommendation 基于会话的推荐中具有顺序行为和侧信息的共同注意模型

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00023

Lin Li, Yuliang Shi, Kun Zhang, Yongjian Ren

{"title":"A Co-Attention Model with Sequential Behaviors and Side Information for Session- based Recommendation","authors":"Lin Li, Yuliang Shi, Kun Zhang, Yongjian Ren","doi":"10.1109/ICWS49710.2020.00023","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00023","url":null,"abstract":"Session-based recommendation aims to recommend the next item a user might be interested in given limited session information, e.g., only clicks are available. Existing researches usually use the RNN-based method or combine user long-term and current session interests to learn certain user preferences. However, due to the uncertainty of user behaviors and the limited behavior information, these methods may lose information of the relevant behavioral features and introduce some noise of unrelated behaviors. For online platforms, such as knowledge base platforms, these items are not isolated but connected with each other. We can use the correlation between items to capture the potential long-distance interests of the user. Therefore, in this paper, we propose a co-attention model with sequential behaviors and side information to obtain a complete representation of user's preferences. For obtaining relevant side information outside the session, we aggregate the corresponding entities and relations of each item to get the representation of the neighborhood information. Finally, extensive experiments are carried out on two real datasets, and the experimental results demonstrate the validity of our model. In particular, the proposed model performs well in cold start scenarios and is well interpreted for the recommended results.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114604385","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: 2

Fitness-guided Resilience Testing of Microservice-based Applications 基于微服务的应用的适应度引导弹性测试

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00027

Zhenyue Long, Guoquan Wu, Xiaojiang Chen, Chengxu Cui, W. Chen, Jun Wei

引用次数: 6

Cognitive Service in Mobile Edge Computing 移动边缘计算中的认知服务

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00031

Chuntao Ding, Ao Zhou, Xiao Ma, Shangguang Wang

{"title":"Cognitive Service in Mobile Edge Computing","authors":"Chuntao Ding, Ao Zhou, Xiao Ma, Shangguang Wang","doi":"10.1109/ICWS49710.2020.00031","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00031","url":null,"abstract":"Cognitive services have revolutionized the way we live, work and interact with the world. In recent years, deep neural networks have become the mainstream approach in cognitive service, and mobile edge computing facilitates a variety of cognitive services for users by offloading computation tasks from resource-limited mobile devices to relatively wealthy edge servers. Combining the two to provide users with a higher quality of cognitive service is an issue worth researching. However, many related studies are not easy to provide fast responses because in these systems, edge servers are only used to pre-process data, and the cloud server is used to perform tasks. In this paper, we aim to study deploying deep neural network models on edge servers to provide fast services. However, a single edge server collects only a small amount of data, which results in low inference accuracy. To address this problem, we propose a cloud and edge collaboration framework. The key idea of the proposed framework is to use a cloud model to assist in training an edge model to improve the latter's inference accuracy and enable the latter to provide fast response and high-performance cognitive service. Experimental results demonstrate the effectiveness of our proposed framework.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"451 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127611474","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: 7

Computing Power Allocation and Traffic Scheduling for Edge Service Provisioning 边缘业务发放的计算能力分配与流量调度

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00058

Zhengzhe Xiang, Shuiguang Deng, Fangqiao Jiang, Honghao Gao, J. Taheri, Jianwei Yin

{"title":"Computing Power Allocation and Traffic Scheduling for Edge Service Provisioning","authors":"Zhengzhe Xiang, Shuiguang Deng, Fangqiao Jiang, Honghao Gao, J. Taheri, Jianwei Yin","doi":"10.1109/ICWS49710.2020.00058","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00058","url":null,"abstract":"The increasing number of mobile web services makes it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on the mobile edge computing (MEC) paradigm, in which the latency can be reduced and the computation can be offloaded with the help of services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigate the edge-cloud cooperation mechanism in service provisioning as well as the billing model of it. To minimize the average service response time and make the expense acceptable, we model and formulate the performance-cost service provisioning problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. Then we propose an efficient online algorithm, called PCA- CATS, to decompose this problem into two individual subproblems. We conduct a series of experiments to evaluate the performance of our approach. The results show that PCA- CATS can easily balance the performance and expense with a factor V, and can reduce up to 53.3 % service response time as compared with the baselines.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132198374","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: 10

A-HSG: Neural Attentive Service Recommendation based on High-order Social Graph A-HSG:基于高阶社交图的神经关注服务推荐

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00051

Chunyu Wei, Yushun Fan, Jia Zhang, Haozhe Lin

{"title":"A-HSG: Neural Attentive Service Recommendation based on High-order Social Graph","authors":"Chunyu Wei, Yushun Fan, Jia Zhang, Haozhe Lin","doi":"10.1109/ICWS49710.2020.00051","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00051","url":null,"abstract":"With the widespread application of Service-Oriented Architecture, the quantity of web services keeps increasing rapidly over the Internet. Providing personalized service recommendation to users remains to be an important research topic. Recent studies have proved social connections helpful for modeling users' potential preference thus improving the performance of service recommendation. To date, however, one special type of social relation, called high-order social relation, has not been thoroughly studied. In reality, a user's preference may not only be affected by the user's direct neighbors, but also indirect ones. Furthermore, such influences may not remain static in the context of various attentions. To tackle such issues, we have developed a novel neural Attentive network based on High-order Social Graph (A-HSG) toward offering social-aware service recommendation. First, a graph convolution-based, multi-hop propagation module is devised to extract the high-order similarity signals from users' local social networks, and inject them into the users' general representations. Second, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users' specific preference. Extensive experiments over a real-life service dataset show that A-HSG outperforms baseline methods in terms of prediction accuracy.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"72 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131592888","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: 5

Hierarchical Business Process Discovery: Identifying Sub-processes Using Lifecycle Information 分层业务流程发现:使用生命周期信息识别子流程

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00062

Cong Liu

引用次数: 5

Latent Group Recommendation based on Double Fuzzy Clustering and Matrix Tri-factorization 基于双模糊聚类和矩阵三分解的潜在群体推荐

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00077

Haiyan Wang, Jinxia Zhu, Zhousheng Wang

{"title":"Latent Group Recommendation based on Double Fuzzy Clustering and Matrix Tri-factorization","authors":"Haiyan Wang, Jinxia Zhu, Zhousheng Wang","doi":"10.1109/ICWS49710.2020.00077","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00077","url":null,"abstract":"Group recommendation has received great attention owing to its practical value in real applications. However, group members are implicit and groups are formed occasionally in some scenarios. Existing solutions for latent group recommendation assumes a user belongs to a specific group, and totally ignore the possible correlation between the user'$s$ preferences and other groups' preferences. In addition, existing methods cannot deal with new items cold-start problem effectively because they only focus on which items are favored by the group without considering the hidden related information between items. These weaknesses usually lead to poor performance of latent group recommendation. To address the problems above, this paper proposes a latent group recommendation method based on double fuzzy clustering and matrix tri-factorization (DFCMTF -LGR). Firstly, this method utilizes unsupervised learning to implement potential feature extraction and double fuzzy clustering for users and items. Secondly, a novel matrix tri-factorization method is presented to adjust the membership of user-to-group, item-to-item category, and the incidence of group-to-item category is obtained. Finally, latent groups are detected according to user-to-group membership, and group rating can be generated in accordance with group-to-item category incidence matrix and item-to-item category membership. Experimental results on real datasets demonstrate that our proposed DFCMTF-LGR has better performance compared with state-of-the art methods.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123769369","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: 2

Budgeted Data Caching based on k-Median in Mobile Edge Computing 移动边缘计算中基于k-Median的预算数据缓存

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00033

Xiaoyu Xia, Feifei Chen, Guangming Cui, Mohamed Almorsy, J. Grundy, Hai Jin, Qiang He

{"title":"Budgeted Data Caching based on k-Median in Mobile Edge Computing","authors":"Xiaoyu Xia, Feifei Chen, Guangming Cui, Mohamed Almorsy, J. Grundy, Hai Jin, Qiang He","doi":"10.1109/ICWS49710.2020.00033","DOIUrl":"https://doi.org/10.1109/ICWS49710.2020.00033","url":null,"abstract":"In mobile edge computing (MEC), edge servers are deployed at base stations to provide highly accessible computational resources and storage capacities to nearby mobile devices. Caching data on edge servers can ensure the service quality and network latency for those mobile devices. However, an app vendor needs to ensure that the data caching cost does not exceed its data caching budget. In this paper, we present the budgeted edge data caching (BEDC) problem as a constrained optimization problem to maximize the overall reduction in data retrieval for all its app users within the budget, and prove that it is NP-hard. Then, we provide an approach named IP-BEDC for solving the BEDC problem optimally based on Integer Programming. We also provide an O(k) -approximation algorithm, namely α-BEDC, to find near-optimal solutions to the BEDC problems efficiently. Our proposed approaches are evaluated on a real-world data set and a synthesized data set. The results demonstrate that our approaches can solve the BEDC problem effectively and efficiently while significantly outperforming five representative approaches.","PeriodicalId":338833,"journal":{"name":"2020 IEEE International Conference on Web Services (ICWS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115646482","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: 9

Computing Admissible Temporal SLAs for Web Service Compositions 为Web服务组合计算可接受的临时sla

2020 IEEE International Conference on Web Services (ICWS) Pub Date : 2020-10-01 DOI: 10.1109/ICWS49710.2020.00048

M. Franceschetti, Johann Eder

引用次数: 1