2015 IEEE International Conference on Web Services最新文献

Clustering Service Networks with Entity, Attribute, and Link Heterogeneity 具有实体、属性和链路异构的集群服务网络

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.43

Yang Zhou, Ling Liu, C. Pu, Xianqiang Bao, Kisung Lee, Balaji Palanisamy, Emre Yigitoglu, Qi Zhang

{"title":"Clustering Service Networks with Entity, Attribute, and Link Heterogeneity","authors":"Yang Zhou, Ling Liu, C. Pu, Xianqiang Bao, Kisung Lee, Balaji Palanisamy, Emre Yigitoglu, Qi Zhang","doi":"10.1109/ICWS.2015.43","DOIUrl":"https://doi.org/10.1109/ICWS.2015.43","url":null,"abstract":"Many popular web service networks are content-rich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose Service Cluster, a novel heterogeneous service network clustering algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration. Extensive evaluation on real datasets demonstrates that Service Cluster outperforms existing representative methods in terms of both effectiveness and efficiency.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123568527","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 Resilient Framework for Fault Handling in Web Service Oriented Systems 面向Web服务系统的故障处理弹性框架

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.93

Weidong Wang, Liqiang Wang, W. Lu

{"title":"A Resilient Framework for Fault Handling in Web Service Oriented Systems","authors":"Weidong Wang, Liqiang Wang, W. Lu","doi":"10.1109/ICWS.2015.93","DOIUrl":"https://doi.org/10.1109/ICWS.2015.93","url":null,"abstract":"Resilience is an important factor in designing web service oriented systems due to frequent failures arising in runtime. These failures derive from the stochastic and uncertainty nature of a composite web service. Service providers need to rapidly address issue when a fault occurs in system running. But it is not easy to locate and fix the faults only using the log generated by the system. In this paper, we propose a resilient framework to automatically generate a fault handling strategy for each failed service to improve the efficiency of fault handling. In the framework, we design and implement three components including exception analyzer, decision maker, and strategy selector. First, The exception analyzer builds a record, derived from the system log generated by an application, for each failed service. Next, the decision maker adopts a k-means clustering approach to construct a decision including the fault handling to each failed service in a scope. Then, the strategy selector uses an integer program solver to generate the solution to strategy selection problem that is boiled down to the optimization problem. The experiment shows that the framework can improve resilience of Web service-oriented systems under acceptable overheads, and meanwhile the accuracy of fault handling strategy is over 95%.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125356649","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 Collaborative Approach to Predicting Service Price for QoS-Aware Service Selection 面向质量感知服务选择的服务价格预测协同方法

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.15

Puwei Wang, A. Kalia, Munindar P. Singh

引用次数: 7

Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness 基于刺激和时间意识的自适应志愿服务组合

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.18

Abdessalam Elhabbash, R. Bahsoon, P. Tiňo, Peter R. Lewis

引用次数: 9

Learning Sparse Functional Factors for Large-Scale Service Clustering 大规模服务聚类的稀疏功能因子学习

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.36

Qi Yu, Hongbing Wang, Liang Chen

{"title":"Learning Sparse Functional Factors for Large-Scale Service Clustering","authors":"Qi Yu, Hongbing Wang, Liang Chen","doi":"10.1109/ICWS.2015.36","DOIUrl":"https://doi.org/10.1109/ICWS.2015.36","url":null,"abstract":"The past decade has witnessed a fast growth of web-based services, making discovery of user desired services from a large and diverse service space a fundamental challenge. Service clustering has been demonstrated as a promising solution by automatically detecting functionally similar services so that they can be searched and discovered together. In this way, both the efficiency and accuracy of service discovery can be improved. However, the autonomous nature of service providers leads to highly diverse usage of terms in their respective service descriptions. Furthermore, a typical service description is comprised of very limited terms due to the small number of (and focused) functionalities offered by the service. These unique characteristics make service descriptions different from regular text documents, which poses additional challenges when clustering large-scale services. Recent works show that service clustering can benefit from discovery and use of functionality-related latent factors to represent services as opposed to a large and diverse set of terms. Nonetheless, how to determine the total number of latent functional factors and sparsely assign them to each service description arises as a central challenge, especially for a large service space where there is no easy way to enumerate the types of different functionalities. In this paper, we propose a machine learning method that automatically learns the number of latent functional factors in a service space. It also enforces the sparsity constraint, which allows each service to be represented by a small number of latent functional factors. The sparsity constraint is in line with the fact that most real-world services only provide limited functionalities. We conduct extensive experiments on two sets of real-world service data to demonstrate the effectiveness of the proposed service clustering approach.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130583861","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

Integrator: An Architecture for an Integrated Cloud/On-Premise Data-Service 集成商:用于集成云/本地数据服务的体系结构

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.23

A. Leff, J. Rayfield

{"title":"Integrator: An Architecture for an Integrated Cloud/On-Premise Data-Service","authors":"A. Leff, J. Rayfield","doi":"10.1109/ICWS.2015.23","DOIUrl":"https://doi.org/10.1109/ICWS.2015.23","url":null,"abstract":"Large enterprises have built very large \"on-premise\" data-sets that are critical to many business functions. With the availability of cloud-based storage, many of these enterprises are considering whether and how to make some of this data available on the cloud. One motivation is to offload the processing of new mobile application workloads from the on-premise system to the cloud. Another motivation is to improve the performance of these mobile applications. However, because of the importance of this data, and because of regulatory constraints, many enterprises are unwilling to simply move their data from an on-premise environment to the cloud. Instead, they prefer to keep the \"master\" version of the data on-premise, while projecting a subset of the data to the cloud. Several challenges face these enterprises. First, how can large data-sets be efficiently made available on the cloud with minimal disruption to the ongoing on-premise business function? Second, how can this data be represented in a way that will be useful to cloud developers? Typically, cloud developers want data represented in a way that is easily consumable by REST APIs, but the on-premise representation may not be amenable to such usage. Our INTEGRATOR project addresses these challenges by providing an integrated cloud/on-premise data-service. Importantly, the INTEGRATOR architecture is broadly applicable across various back-end systems. In this paper we describe the INTEGRATOR architecture and a prototype implementation for a specific on-premise system. We examine alternative architectures - \"table based\" and \"business object based\" - and explain why we chose the business object approach.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121953228","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: 8

Formal Behavioral Modeling for Verifying SCA Composition with Event-B 用事件b验证SCA组合的形式化行为建模

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.13

Mohamed Graiet, Aida Lahouij, Imed Abbassi, Lazhar Hamel, Mourad Kmimech

引用次数: 7

A Maturity Model for Semantic RESTful Web APIs 语义RESTful Web api的成熟度模型

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.98

I. Salvadori, Frank Siqueira

引用次数: 12

A Novel Lifecycle Framework for Semantic Web Service Annotation Assessment and Optimization 一种新的语义Web服务注释评估与优化生命周期框架

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.55

Juan Chen, Zhiyong Feng, Shizhan Chen, Keman Huang, Wei Tan, Jia Zhang

{"title":"A Novel Lifecycle Framework for Semantic Web Service Annotation Assessment and Optimization","authors":"Juan Chen, Zhiyong Feng, Shizhan Chen, Keman Huang, Wei Tan, Jia Zhang","doi":"10.1109/ICWS.2015.55","DOIUrl":"https://doi.org/10.1109/ICWS.2015.55","url":null,"abstract":"Semantic annotation plays an important role for semantic-aware web service discovery, recommendation and composition. In recent years, many approaches and tools have emerged to assist in semantic annotation creation and analysis. However, the Quality of Semantic Annotation (QoSA) is largely overlooked despite of its significant impact on the effectiveness of semantic-aware solutions. Moreover, improving the QoSA is time-consuming and requires significant domain knowledge. Therefore, how to verify and improve the QoSA has become a critical issue for semantic web services. In order to facilitate this process, this paper presents a novel lifecycle framework aiming at QoSA assessment and optimization. The QoSA is formally defined as the success rate of web service invocations, associated with a verification framework. Based on a local instance repository constructed from the execution information of the invocations, a two-layer optimization method including a local-feedback strategy and a global-feedback one is proposed to improve the QoSA. Experiments on real-world web services show that our framework can gain 65.95%~148.16% improvement in QoSA, compared with the original annotation without optimization.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125795661","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: 6

Predicting QoS Values via Multi-dimensional QoS Data for Web Service Recommendations 基于Web服务推荐的多维QoS数据预测QoS值

2015 IEEE International Conference on Web Services Pub Date : 2015-06-27 DOI: 10.1109/ICWS.2015.42

You Ma, Shangguang Wang, Fangchun Yang, Rong N. Chang

{"title":"Predicting QoS Values via Multi-dimensional QoS Data for Web Service Recommendations","authors":"You Ma, Shangguang Wang, Fangchun Yang, Rong N. Chang","doi":"10.1109/ICWS.2015.42","DOIUrl":"https://doi.org/10.1109/ICWS.2015.42","url":null,"abstract":"Fast deployment of mobile Internet makes Web services often consumed under a multi-dimensional spatiotemporal model, wherein a specific service client could keep active while its location is changing. Recommending Web services for such clients must be able to predict unknown QoS values with the target client's service requesting time and location taken into account, e.g., Performing the prediction via a set of measured multi-dimensional QoS data. Most QoS prediction methods focus on the QoS characteristics for one specific dimension, e.g., Time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient service recommendation for Web service based mobile clients via tensor decomposition and reconstruction optimization algorithms. Comparative experimental evaluation results show that the proposed QoS prediction approach could result in much better accuracy in recommending Web services than several other representative ones.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125052188","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: 22