Yang Zhou, Ling Liu, C. Pu, Xianqiang Bao, Kisung Lee, Balaji Palanisamy, Emre Yigitoglu, Qi Zhang
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.
{"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}
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%.
{"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}
In QoS-aware service selection, a service requester seeks to maximize its utility by selecting a service provider that charges the lowest service price while meeting the requester's QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and candidate service providers have a conflicting interest over service prices. Since a provider would not reveal its minimum acceptabl price, it is important for a requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experience. The experimental results show our approach can find the optimal service providers efficiently and effectively.
{"title":"A Collaborative Approach to Predicting Service Price for QoS-Aware Service Selection","authors":"Puwei Wang, A. Kalia, Munindar P. Singh","doi":"10.1109/ICWS.2015.15","DOIUrl":"https://doi.org/10.1109/ICWS.2015.15","url":null,"abstract":"In QoS-aware service selection, a service requester seeks to maximize its utility by selecting a service provider that charges the lowest service price while meeting the requester's QoS requirements. In existing selection approaches, a service requester focuses on finding providers based on their QoS and thereby ignores their service prices that could change with their QoS. High QoS may provide more benefits, but may require a high service price. As a result, the highest QoS may not produce the maximum utility. A service requester and candidate service providers have a conflicting interest over service prices. Since a provider would not reveal its minimum acceptabl price, it is important for a requester to predict the minimum price for a service that meets its QoS requirements. We propose a collaborative approach to predicting a provider's minimum price for a desired QoS based on prior usage experience. The experimental results show our approach can find the optimal service providers efficiently and effectively.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"15 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":"128219733","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}
Abdessalam Elhabbash, R. Bahsoon, P. Tiňo, Peter R. Lewis
Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus- and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.
{"title":"Self-Adaptive Volunteered Services Composition through Stimulus- and Time-Awareness","authors":"Abdessalam Elhabbash, R. Bahsoon, P. Tiňo, Peter R. Lewis","doi":"10.1109/ICWS.2015.18","DOIUrl":"https://doi.org/10.1109/ICWS.2015.18","url":null,"abstract":"Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus- and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"54 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":"128745532","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}
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.
{"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}
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.
{"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}
With the emergence of Service Component Architecture (SCA), all interests were focused on representing this architecture in a formal way in order to be able to prevent the specifications failures. In this context, our recent works were interested in formalizing structural properties of the SCA specifications, particularly in defining structural compatibility between connected services. In fact, verifying structural compatibility is necessary but not sufficient. In this paper we intend to represent, in a first step, the SCA behavioral properties by means of Event-B invariants and events. In a second step, we established behavioral compatibility between services interacting together which is considered as a delicate task and has a great importance in guaranteeing reliable communication between services. The consistency and the validity of the obtained model have been proved by the Event-B dedicated tools.
{"title":"Formal Behavioral Modeling for Verifying SCA Composition with Event-B","authors":"Mohamed Graiet, Aida Lahouij, Imed Abbassi, Lazhar Hamel, Mourad Kmimech","doi":"10.1109/ICWS.2015.13","DOIUrl":"https://doi.org/10.1109/ICWS.2015.13","url":null,"abstract":"With the emergence of Service Component Architecture (SCA), all interests were focused on representing this architecture in a formal way in order to be able to prevent the specifications failures. In this context, our recent works were interested in formalizing structural properties of the SCA specifications, particularly in defining structural compatibility between connected services. In fact, verifying structural compatibility is necessary but not sufficient. In this paper we intend to represent, in a first step, the SCA behavioral properties by means of Event-B invariants and events. In a second step, we established behavioral compatibility between services interacting together which is considered as a delicate task and has a great importance in guaranteeing reliable communication between services. The consistency and the validity of the obtained model have been proved by the Event-B dedicated tools.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"2 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":"131204359","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}
Web APIs provide interfaces for interaction among systems based on the existing infrastructure for hosting Web sites and applications. The REST architectural style is the most employed approach for building Web APIs. However, the flexibility provided by REST may result in implementations with low quality design, limited reuse and poor documentation. This paper describes a maturity model for classifying Web APIs, aimed at promoting the adherence to REST architectural principles and the adoption of semantic Web technology in order to improve the design, reuse and documentation of Web APIs.
Web api基于托管Web站点和应用程序的现有基础设施，为系统之间的交互提供接口。REST架构风格是构建Web api最常用的方法。然而，REST提供的灵活性可能导致实现的设计质量低、重用受限和文档编制不完善。本文描述了一个用于分类Web api的成熟度模型，旨在促进对REST架构原则的遵守和对语义Web技术的采用，以改进Web api的设计、重用和文档化。
{"title":"A Maturity Model for Semantic RESTful Web APIs","authors":"I. Salvadori, Frank Siqueira","doi":"10.1109/ICWS.2015.98","DOIUrl":"https://doi.org/10.1109/ICWS.2015.98","url":null,"abstract":"Web APIs provide interfaces for interaction among systems based on the existing infrastructure for hosting Web sites and applications. The REST architectural style is the most employed approach for building Web APIs. However, the flexibility provided by REST may result in implementations with low quality design, limited reuse and poor documentation. This paper describes a maturity model for classifying Web APIs, aimed at promoting the adherence to REST architectural principles and the adoption of semantic Web technology in order to improve the design, reuse and documentation of Web APIs.","PeriodicalId":250871,"journal":{"name":"2015 IEEE International Conference on Web Services","volume":"17 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":"127223395","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}
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.
{"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}
You Ma, Shangguang Wang, Fangchun Yang, Rong N. Chang
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.
{"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}
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