Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00020
Xiao Ren, Wenjun Zhang, Liang Bao, Jinqiu Song, Shuai Wang, Rong Cao, Xinlei Wang
When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based service composition problem. Currently, there are many classical methods and reinforcement learning methods applied to the QoS-based service composition problem. However, these methods require long computation time. We address three challenges in building an end-to-end supervised learning framework. 1) The number of Web services composing different composite services varies. 2) The topological relationships among Web services are difficult to express and difficult to integrate into neural networks. 3) The number of Web services providing each sub-function in composite services varies. Finally, we propose DeepQSC, a deep supervised learning framework based on graph convolutional networks and attention mechanisms. The framework can form high QoS composite services with limited computation time. We conducted experiments on a real-world dataset. The experiments show that DeepQSC has a significant advantage over six current state-of-the-art algorithms.
{"title":"DeepQSC: a GNN and Attention Mechanism-based Framework for QoS-aware Service Composition","authors":"Xiao Ren, Wenjun Zhang, Liang Bao, Jinqiu Song, Shuai Wang, Rong Cao, Xinlei Wang","doi":"10.1109/ICSS53362.2021.00020","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00020","url":null,"abstract":"When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based service composition problem. Currently, there are many classical methods and reinforcement learning methods applied to the QoS-based service composition problem. However, these methods require long computation time. We address three challenges in building an end-to-end supervised learning framework. 1) The number of Web services composing different composite services varies. 2) The topological relationships among Web services are difficult to express and difficult to integrate into neural networks. 3) The number of Web services providing each sub-function in composite services varies. Finally, we propose DeepQSC, a deep supervised learning framework based on graph convolutional networks and attention mechanisms. The framework can form high QoS composite services with limited computation time. We conducted experiments on a real-world dataset. The experiments show that DeepQSC has a significant advantage over six current state-of-the-art algorithms.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431817","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00026
Junfang Wu, Chunyang Ye, Hui Zhou
Sentiment analysis (SA) has been applied to various fields of software engineering (SE), such as app reviews, stack overflow Q&A website and API comments. General SA tools are trained based on movie or product review data. Research has shown that these SA tools can produce negative results when applied to the field of SE. In order to overcome the above limitations, developers need to customize tools (e.g., SentiStrength-SE, SentiCR, Senti4SD). In recent years, the pre-trained transformer-based models have brought great breakthroughs in the field of natural language processing. Therefore, we intend to fine-tune the pre-trained model BERT for downstream text classification tasks. We compare the performance of SE-specific tools. Meanwhile, we also studied the performance of SE-specific tools in a cross-platform setting. Experimental results show that our approach (BERT-FT) outperforms the existing state-of-the-art models in terms of F1-scores.
{"title":"BERT for Sentiment Classification in Software Engineering","authors":"Junfang Wu, Chunyang Ye, Hui Zhou","doi":"10.1109/ICSS53362.2021.00026","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00026","url":null,"abstract":"Sentiment analysis (SA) has been applied to various fields of software engineering (SE), such as app reviews, stack overflow Q&A website and API comments. General SA tools are trained based on movie or product review data. Research has shown that these SA tools can produce negative results when applied to the field of SE. In order to overcome the above limitations, developers need to customize tools (e.g., SentiStrength-SE, SentiCR, Senti4SD). In recent years, the pre-trained transformer-based models have brought great breakthroughs in the field of natural language processing. Therefore, we intend to fine-tune the pre-trained model BERT for downstream text classification tasks. We compare the performance of SE-specific tools. Meanwhile, we also studied the performance of SE-specific tools in a cross-platform setting. Experimental results show that our approach (BERT-FT) outperforms the existing state-of-the-art models in terms of F1-scores.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115578697","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00013
Xinjie Zhou, Guyue Gao, X. Ming, Liya Wang, Dao Yin, Xiaohong Ma
As a knowledge-intensive activity, successful complex product design relies on the ability to effectively manage and share engineering knowledge and experience among the whole company. Since complex product design tasks are usually decomposed into work packages, organizing the knowledge in a task-oriented way is conductive to providing the knowledge service to designers. To this end, we propose a task-oriented complex product design knowledge space model with the dimensions of the design phase, design object and discipline area. As the basic unit of knowledge space model, the design task knowledge unit is classified according to product design knowledge service requirements. After that, a knowledge graph-based knowledge representation framework of complex product design is proposed, which includes knowledge graph layer, RDF (Resource Description Framework) layer and the resource layer. The data schema of complex product design knowledge graph is designed and modeled based on ontology. Finally, a case study of gas turbine blade modeling is given to validate the feasibility of the proposed method.
{"title":"Task-oriented Knowledge Representation and Ontology Modeling for Complex Product Design","authors":"Xinjie Zhou, Guyue Gao, X. Ming, Liya Wang, Dao Yin, Xiaohong Ma","doi":"10.1109/ICSS53362.2021.00013","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00013","url":null,"abstract":"As a knowledge-intensive activity, successful complex product design relies on the ability to effectively manage and share engineering knowledge and experience among the whole company. Since complex product design tasks are usually decomposed into work packages, organizing the knowledge in a task-oriented way is conductive to providing the knowledge service to designers. To this end, we propose a task-oriented complex product design knowledge space model with the dimensions of the design phase, design object and discipline area. As the basic unit of knowledge space model, the design task knowledge unit is classified according to product design knowledge service requirements. After that, a knowledge graph-based knowledge representation framework of complex product design is proposed, which includes knowledge graph layer, RDF (Resource Description Framework) layer and the resource layer. The data schema of complex product design knowledge graph is designed and modeled based on ontology. Finally, a case study of gas turbine blade modeling is given to validate the feasibility of the proposed method.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125395957","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00017
Shanyan Lai, Hongyu Jiang, Chunyang Ye, Hui Zhou
Stock trend forecasting plays a great role in maximizing the profit of stock investment. However, due to the high volatility and non-stationarity of the stock market, accurate trend prediction is very difficult. With the development of the Internet and deep learning technology, people can use deep learning methods to reveal market trends and volatility from the explosive information on the Internet. Unfortunately, there is a large amount of content related to the stock market, and a large part of it is useless information. As a result, how to extract the effective information and combine this information as different characteristics to effectively predict stock trends has become the biggest challenge. In order to cope with these challenges, we use TextCNN as the news text feature extractor for feature extraction of news information, and propose a prediction method based on multi-feature fusion: Bi-LSTNAA, to predict the Chinese stock market. Extensive experiments on actual stock market data show that the our method has a greater improvement in the accuracy of stock trend prediction.
{"title":"Chinese stock market prediction based on multifeature fusion and TextCNN","authors":"Shanyan Lai, Hongyu Jiang, Chunyang Ye, Hui Zhou","doi":"10.1109/ICSS53362.2021.00017","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00017","url":null,"abstract":"Stock trend forecasting plays a great role in maximizing the profit of stock investment. However, due to the high volatility and non-stationarity of the stock market, accurate trend prediction is very difficult. With the development of the Internet and deep learning technology, people can use deep learning methods to reveal market trends and volatility from the explosive information on the Internet. Unfortunately, there is a large amount of content related to the stock market, and a large part of it is useless information. As a result, how to extract the effective information and combine this information as different characteristics to effectively predict stock trends has become the biggest challenge. In order to cope with these challenges, we use TextCNN as the news text feature extractor for feature extraction of news information, and propose a prediction method based on multi-feature fusion: Bi-LSTNAA, to predict the Chinese stock market. Extensive experiments on actual stock market data show that the our method has a greater improvement in the accuracy of stock trend prediction.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128621183","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00018
Dong Wang, Qing Li, Chenyang Xu, Piao Wang, Zhuohao Wang
A core work of the science and technology management system is to support the integration and utilization of massive data from distributed systems using data warehouse technology. In this paper, we focus on this work. First, we introduce the background of science and technology management by illustrating the scheme of project management business flows. Then, to define the science and technology data structure, we propose a customized star model in which we add various dimensional tables to connect the different attributes with science and technology projects. Particularly, a 4-D data cube is introduced to satisfy complicated query conditions in science and technology domain. Furthermore, to integrate data from distributed systems, we propose an architecture of data warehouse of science and technology projects (STPDW)—a five layered system that gathers raw data from the science and technology management systems, and processes the data in a standard workflow by using Extract Transform and Load (ETL) tools with predefined rules. Finally, a prototype system has also been designed to achieve the functionality of the STPDW.
{"title":"Research of Data Warehouse for Science and Technology Management System","authors":"Dong Wang, Qing Li, Chenyang Xu, Piao Wang, Zhuohao Wang","doi":"10.1109/ICSS53362.2021.00018","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00018","url":null,"abstract":"A core work of the science and technology management system is to support the integration and utilization of massive data from distributed systems using data warehouse technology. In this paper, we focus on this work. First, we introduce the background of science and technology management by illustrating the scheme of project management business flows. Then, to define the science and technology data structure, we propose a customized star model in which we add various dimensional tables to connect the different attributes with science and technology projects. Particularly, a 4-D data cube is introduced to satisfy complicated query conditions in science and technology domain. Furthermore, to integrate data from distributed systems, we propose an architecture of data warehouse of science and technology projects (STPDW)—a five layered system that gathers raw data from the science and technology management systems, and processes the data in a standard workflow by using Extract Transform and Load (ETL) tools with predefined rules. Finally, a prototype system has also been designed to achieve the functionality of the STPDW.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126281250","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00024
Lei Wang, Kun Han, Qing Qian, Qiang Zhang, Jishuai Wang, Wenbo Cheng
China has initially formed a medical instruments innovation, research and development system. There is an urgent need to build a national data platform and information system to integrate and analyze the information of demonstration and promotion, application evaluation and scientific and technological project achievements of domestic innovative medical instruments, so as to provide assistance for the improvement of the quality of medical instruments and the competitiveness of enterprises in China. This paper mainly introduces the work and progress of innovative medical instruments data platform and information system from the perspective of service computing and service-oriented system design, so as to strengthen the service integration and collaborative innovation of innovative medical instruments industry chain, supply chain, service chain, innovation chain and talent chain. The construction of domestic medical instruments service ecosystem is a systematic innovation project. This paper puts forward some thoughts and suggestions on the construction of domestic medical instruments service ecosystem.
{"title":"Innovative medical instruments data platform and service ecosystem construction","authors":"Lei Wang, Kun Han, Qing Qian, Qiang Zhang, Jishuai Wang, Wenbo Cheng","doi":"10.1109/ICSS53362.2021.00024","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00024","url":null,"abstract":"China has initially formed a medical instruments innovation, research and development system. There is an urgent need to build a national data platform and information system to integrate and analyze the information of demonstration and promotion, application evaluation and scientific and technological project achievements of domestic innovative medical instruments, so as to provide assistance for the improvement of the quality of medical instruments and the competitiveness of enterprises in China. This paper mainly introduces the work and progress of innovative medical instruments data platform and information system from the perspective of service computing and service-oriented system design, so as to strengthen the service integration and collaborative innovation of innovative medical instruments industry chain, supply chain, service chain, innovation chain and talent chain. The construction of domestic medical instruments service ecosystem is a systematic innovation project. This paper puts forward some thoughts and suggestions on the construction of domestic medical instruments service ecosystem.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132128305","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00021
Yuanyuan Lan, Lei Fang, Mingzhu Zhang, Jian-Rong Su, Zhongguo Yang, Han Li
In the cloud computing environment, the release and dynamic deployment of microservices face many challenges, and the services’ dependency is an important consideration for service deployment. The service call chain logs record rich information, such as the time and delay of each service call in the business tracking process, and present the logs in the form of a call chain. Existing research does not fully consider the local composite service dependencies and their discontinuous dependencies in the service chain. In order to obtain these complex dependencies and provide basic supporting data for the dynamic deployment and adjustment of microservices, a dependency model of microservices is proposed, and a dependency mining method based on call chain logs is designed to extract local dependencies and the discontinuous dependency relationship. The effectiveness of the algorithm is verified by testing on Alibaba Cloud Computing’s public data set.
{"title":"Service dependency mining method based on service call chain analysis","authors":"Yuanyuan Lan, Lei Fang, Mingzhu Zhang, Jian-Rong Su, Zhongguo Yang, Han Li","doi":"10.1109/ICSS53362.2021.00021","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00021","url":null,"abstract":"In the cloud computing environment, the release and dynamic deployment of microservices face many challenges, and the services’ dependency is an important consideration for service deployment. The service call chain logs record rich information, such as the time and delay of each service call in the business tracking process, and present the logs in the form of a call chain. Existing research does not fully consider the local composite service dependencies and their discontinuous dependencies in the service chain. In order to obtain these complex dependencies and provide basic supporting data for the dynamic deployment and adjustment of microservices, a dependency model of microservices is proposed, and a dependency mining method based on call chain logs is designed to extract local dependencies and the discontinuous dependency relationship. The effectiveness of the algorithm is verified by testing on Alibaba Cloud Computing’s public data set.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114138072","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00012
W. Tan, Yi Chen, Erfu Yang
With the development of network economy, many manufacturers provide various kinds of cloud manufacturing services for users to use on demand. However, facing the increasingly diverse needs of users, traditional atomic services and their combined services can no longer meet the needs of the market. Cloud service providers urgently need to provide abundant cloud services with different granularity. Nevertheless, there are few studies on coarse-grained service construction. In this paper, quality of service (QoS) and business association aware based enterprise process granular service superiority selection evaluation model is provided. On the basis of this, the preferred framework is put forward, including QoS-aware computing, process service node selection and assemblying, process granular service merging, process granular service inspection. And the related algorithms are designed. Experiments show that the proposed superiority selection evaluation model and related algorithms are practical, which can effectively extract the superior granular services needed by enterprises.
{"title":"QoS and Business Association aware based selection of excellent Process Granular Services in Enterprise","authors":"W. Tan, Yi Chen, Erfu Yang","doi":"10.1109/ICSS53362.2021.00012","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00012","url":null,"abstract":"With the development of network economy, many manufacturers provide various kinds of cloud manufacturing services for users to use on demand. However, facing the increasingly diverse needs of users, traditional atomic services and their combined services can no longer meet the needs of the market. Cloud service providers urgently need to provide abundant cloud services with different granularity. Nevertheless, there are few studies on coarse-grained service construction. In this paper, quality of service (QoS) and business association aware based enterprise process granular service superiority selection evaluation model is provided. On the basis of this, the preferred framework is put forward, including QoS-aware computing, process service node selection and assemblying, process granular service merging, process granular service inspection. And the related algorithms are designed. Experiments show that the proposed superiority selection evaluation model and related algorithms are practical, which can effectively extract the superior granular services needed by enterprises.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114490932","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSS53362.2021.00022
Chunjuan Zang, Mingjun Xin
Nowadays, with the reform of the research evaluation mechanism proposed by the nation, the evaluation mechanism of scientific research activities has been developed rapidly in universities which is strongly supported by the nation. Scientific research evaluation affects the development direction of scientific research activities, thus, how to carry out effective scientific research evaluation activities becomes an important issue among the universities. In this study, a neural network-based service evaluation model is proposed for the existing problems in scientific research evaluation service. The model is divided into two parts: obtaining evaluation index weights based on neural networks and obtaining final evaluation results based on the multi-model fusion method. Finally, the experiments on real university research data are conducted. The results show that our model can effectively obtain research evaluation results, which include both faculty evaluation results and team evaluation results.
{"title":"A Neural Network-based Research Performance Service Portfolio Evaluation Model and Its Implementation","authors":"Chunjuan Zang, Mingjun Xin","doi":"10.1109/ICSS53362.2021.00022","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00022","url":null,"abstract":"Nowadays, with the reform of the research evaluation mechanism proposed by the nation, the evaluation mechanism of scientific research activities has been developed rapidly in universities which is strongly supported by the nation. Scientific research evaluation affects the development direction of scientific research activities, thus, how to carry out effective scientific research evaluation activities becomes an important issue among the universities. In this study, a neural network-based service evaluation model is proposed for the existing problems in scientific research evaluation service. The model is divided into two parts: obtaining evaluation index weights based on neural networks and obtaining final evaluation results based on the multi-model fusion method. Finally, the experiments on real university research data are conducted. The results show that our model can effectively obtain research evaluation results, which include both faculty evaluation results and team evaluation results.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117196929","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 the Internet of Services (IoS), service platforms need to integrate services from multiple domains and various service providers to fulfill user requirements. Considering services and service solutions (composed of services) in IoS are complicated, a big challenge is how to design and implement a system to effectively monitor, measure and evaluate the quality of the services and solutions. For this challenge, we introduce a framework and develop a platform for service providers to establish a VQC (value, quality, capability) index hierarchy and efficiently obtain running indices of services and solutions in IoS. The main idea is to divide the indices into three types: value, quality, and capability. Then, service providers can establish a dynamic hierarchy that defines the calculation relationship between indices as well as binds the indices to corresponding services and solutions by this platform so that the system can monitor services and solutions in run-time. Real-time data would be collected and measured. The design and implementation of the platform supporting the above process are introduced in detail. Finally, a case study is proposed to demonstrate the framework and system.
在服务互联网(Internet of Services, IoS)中，业务平台需要整合来自多个领域和不同服务提供商的业务，以满足用户需求。考虑到IoS中的服务和服务解决方案(由服务组成)的复杂性，如何设计和实现一个系统来有效地监控、测量和评估服务和解决方案的质量是一个很大的挑战。针对这一挑战，我们为服务提供商引入框架并开发平台，建立VQC(价值、质量、能力)指标层次，高效获取IoS中服务和解决方案的运行指标。主要思想是将指标分为价值、质量和能力三种类型。然后，服务提供商可以建立一个动态的层次结构，通过该平台定义指标之间的计算关系，并将指标绑定到相应的服务和解决方案，以便系统在运行时监控服务和解决方案。将收集和测量实时数据。详细介绍了支持上述过程的平台的设计与实现。最后，通过一个案例对该框架和系统进行了论证。
{"title":"Quality Monitoring and Measuring for Internet of Services","authors":"Cheng Pan, Hanchuan Xu, Weifeng Li, Zhiying Tu, Xiaofei Xu, Zhongjie Wang","doi":"10.1109/ICSS53362.2021.00025","DOIUrl":"https://doi.org/10.1109/ICSS53362.2021.00025","url":null,"abstract":"In the Internet of Services (IoS), service platforms need to integrate services from multiple domains and various service providers to fulfill user requirements. Considering services and service solutions (composed of services) in IoS are complicated, a big challenge is how to design and implement a system to effectively monitor, measure and evaluate the quality of the services and solutions. For this challenge, we introduce a framework and develop a platform for service providers to establish a VQC (value, quality, capability) index hierarchy and efficiently obtain running indices of services and solutions in IoS. The main idea is to divide the indices into three types: value, quality, and capability. Then, service providers can establish a dynamic hierarchy that defines the calculation relationship between indices as well as binds the indices to corresponding services and solutions by this platform so that the system can monitor services and solutions in run-time. Real-time data would be collected and measured. The design and implementation of the platform supporting the above process are introduced in detail. Finally, a case study is proposed to demonstrate the framework and system.","PeriodicalId":284026,"journal":{"name":"2021 International Conference on Service Science (ICSS)","volume":"46 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131763097","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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