2018 IEEE International Conference on Web Services (ICWS)最新文献_第2页

A Recommendation Algorithm Based on Dynamic User Preference and Service Quality 基于动态用户偏好和服务质量的推荐算法

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00019

Yanmei Zhang, Ya Qian, Yan Wang

引用次数: 8

Web Services for Emergencies: Multi-Transport, Multi-Cloud, Multi-Role 紧急情况Web服务:多传输、多云、多角色

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00054

Derek Jacoby, N. Preston, Madhav Malhotra, Y. Coady

引用次数: 1

A Service-Based Fog Execution Environment for the IoT-Aware Business Process Applications 面向物联网感知业务流程应用的基于服务的雾执行环境

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00052

Yong-Yang Cheng, Shuai Zhao, B. Cheng, Junliang Chen

引用次数: 7

Improving Service Recommendation by Alleviating the Sparsity with a Novel Ontology-Based Clustering 一种基于本体的聚类方法，通过减少稀疏性来改进服务推荐

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00059

R. Rupasingha, Incheon Paik

{"title":"Improving Service Recommendation by Alleviating the Sparsity with a Novel Ontology-Based Clustering","authors":"R. Rupasingha, Incheon Paik","doi":"10.1109/ICWS.2018.00059","DOIUrl":"https://doi.org/10.1109/ICWS.2018.00059","url":null,"abstract":"Web service recommendation in an efficient and accurate manner has become a significant tool with information overload and an increasingly urgent demand to provide appropriate recommendations to users. Among the service recommendation algorithms, Collaborative Filtering (CF) gives credence to user inputs by comparing user's correlations. Performance of the service recommendation approaches becomes deficient due to the data sparsity and cold-start issues, which make the incomplete and inadequate information to analyze a user predicament on Web services. This paper proposes a CF-based recommendation approach that first alleviates the sparsity problem using a novel ontology-based clustering approach that used domain specificity and service similarity for the ontology generation. Then, we propose a trustbased user rating prediction by determining the trust value between users by calculating the correlation of users. The experimental results indicate that the proposed approach can effectively alleviate the sparsity and cold-start problems by lower prediction error compared with existing sparsity managing mechanisms in service recommendations.","PeriodicalId":231056,"journal":{"name":"2018 IEEE International Conference on Web Services (ICWS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134082952","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

(WIP) Correlation-Driven Service Event Routing for Predictive Industrial Maintenance 面向预测性工业维护的关联驱动服务事件路由

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00044

Meiling Zhu, Chen Liu, Shouli Zhang, Yanbo Han

引用次数: 0

Performance Analysis of Service Clouds Serving Composite Service Application Jobs 服务于复合服务应用作业的服务云性能分析

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00036

Xiulin Li, Shijun Liu, Li Pan, Yuliang Shi, Xiangxu Meng

{"title":"Performance Analysis of Service Clouds Serving Composite Service Application Jobs","authors":"Xiulin Li, Shijun Liu, Li Pan, Yuliang Shi, Xiangxu Meng","doi":"10.1109/ICWS.2018.00036","DOIUrl":"https://doi.org/10.1109/ICWS.2018.00036","url":null,"abstract":"Performance analysis is important for service clouds serving composite service application jobs containing parallelizable tasks, for optimizing the degree of parallelism (DOP) and resource allocation schemes could improve performance obviously. In this paper, we describe a novel tandem queuing network with a parallel multi-station multi-server system as an analytical model for service clouds serving composite service application jobs. We design a partition method (termed the 'pleasing partition') to help us propose an analytical model for parallelizable service which is the vital fraction of composite service. After that, we could obtain a complete probability distribution of response time, waiting time and other important performance metrics calculated by our proposed analytical model. Thus, to use this model, cloud operators could determine proper job configurations and resource allocation schemes, for achieving specific QoS (Quality of Service). Extensive simulations are conducted to validate that our analytical model has high accuracy in predicting performance metrics of composite service application jobs.","PeriodicalId":231056,"journal":{"name":"2018 IEEE International Conference on Web Services (ICWS)","volume":"837 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133349148","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

A Probabilistic Model for Service Clustering - Jointly Using Service Invocation and Service Characteristics 服务集群的概率模型——联合使用服务调用和服务特征

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00047

Dongxiao He, Xue Yang, Zhiyong Feng, Shizhan Chen, Keman Huang, Zhenzhu Wang, F. Fogelman-Soulié

{"title":"A Probabilistic Model for Service Clustering - Jointly Using Service Invocation and Service Characteristics","authors":"Dongxiao He, Xue Yang, Zhiyong Feng, Shizhan Chen, Keman Huang, Zhenzhu Wang, F. Fogelman-Soulié","doi":"10.1109/ICWS.2018.00047","DOIUrl":"https://doi.org/10.1109/ICWS.2018.00047","url":null,"abstract":"Service clustering is the foundation of service discovery, recommendation and composition. Most of the existing methods mainly use service attribute information and ignore the semantic-based invocation relationships among service users. In fact, mutual invocation relationships between services occur on operations of the corresponding services, while service attributes are the whole service description. Our main challenge may be to effectively combine these two kinds of data for service clustering. To address this issue, we propose a new probabilistic generative model which contains two closely connected parts, one characterizing operation community memberships by using operation invocation relationships, and the other characterizing service cluster memberships by utilizing service attributes. The correlations between these two parts are characterized by the relationships between operation communities and service clusters. To train this model, we provide a nested expectation-maximization algorithm. Experimental results show its superior performance over the existing methods for service clustering.","PeriodicalId":231056,"journal":{"name":"2018 IEEE International Conference on Web Services (ICWS)","volume":" 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113950250","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: 3

A Service Annotation Quality Improvement Approach Based on Efficient Human Intervention 基于高效人工干预的服务标注质量改进方法

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00021

Xuehao Sun, Shizhan Chen, Zhiyong Feng, Weimin Ge, Keman Huang

{"title":"A Service Annotation Quality Improvement Approach Based on Efficient Human Intervention","authors":"Xuehao Sun, Shizhan Chen, Zhiyong Feng, Weimin Ge, Keman Huang","doi":"10.1109/ICWS.2018.00021","DOIUrl":"https://doi.org/10.1109/ICWS.2018.00021","url":null,"abstract":"Semantic Annotation plays an essential role in automatic service discovery and composition. However, existing approaches and tools cannot achieve high annotation quality to ensure the semantic service application. Meanwhile, the semi-automatic strategies for improving the annotation quality are time-consuming. To further improve the efficiency as well as the quality of the annotation, this paper presents an effective method involving human-computer interaction to further optimize the annotation procedure. Besides employing the feedback and propagation strategy to semi-automatically improve the annotation quality, the strategy to involve the manual annotation is developed when the efficiency of semi-automatically strategy is related low. To optimize the manual annotation procedure, a clustering based approach is presented to select the most impacted candidates to optimize the annotation improvement. In addition, to help the annotators to choose the correct annotation, the local ontology restriction based method is further designed to improve the recommendation performance. The experiments show that our approach effectively involving the human intervention can significantly improve the annotation quality, faster the quality improvement procedure and reduce the manual load by increasing the recommendation accuracy.","PeriodicalId":231056,"journal":{"name":"2018 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127645819","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: 1

QCSS: A QoE-Aware Control Plane for Adaptive Streaming Service over Mobile Edge Computing Infrastructures QCSS:移动边缘计算基础设施上自适应流服务的qos感知控制平面

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00025

Lingyan Zhang, Shangguang Wang, Rong N. Chang

{"title":"QCSS: A QoE-Aware Control Plane for Adaptive Streaming Service over Mobile Edge Computing Infrastructures","authors":"Lingyan Zhang, Shangguang Wang, Rong N. Chang","doi":"10.1109/ICWS.2018.00025","DOIUrl":"https://doi.org/10.1109/ICWS.2018.00025","url":null,"abstract":"Mobile Edge Computing (MEC) is designed to extend the edge of the cloud network to decrease latency and network congestion, which would significantly improve the quality of experience (QoE) of adaptive streaming service for mobile users. This paper proposes QCSS, a QoE-aware control plane for adaptive streaming service over MEC infrastructures. QCSS aims to assure high QoE delivery of online streaming service to mobile users. The design of QCSS features: 1) a timeslot system with a look-ahead window for calculating cost of edge node switch and video quality adaption (to balance network load and reduce latency); 2) conducting service adaption via a set of cooperative action components running on client devices, edge nodes, and center nodes (to ensure a smooth viewing experience); 3) constructing a flexible QoE model and extending the scope and meaning of user-perceived experience. The effectiveness of QCSS has been validated via three real datasets. The validation results show that the proposed QCSS can improve QoE performance and network load performance for adaptive streaming service over MEC Infrastructures.","PeriodicalId":231056,"journal":{"name":"2018 IEEE International Conference on Web Services (ICWS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128817281","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

An Efficient Distributed-Computing Framework for Association-Rule-Based Recommendation 基于关联规则推荐的高效分布式计算框架

2018 IEEE International Conference on Web Services (ICWS) Pub Date : 2018-07-01 DOI: 10.1109/ICWS.2018.00056

Changsheng Li, Weichao Liang, Zhiang Wu, Jie Cao

引用次数: 12