Bluetooth Low Energy beacon service is the newest Online-to-Offline technology. A beacon application in users smartphone connects an offline physical beacon with an online service produced by a service provider. However, since the BLE beacon separately operates by each service provider, users need to run multiple providers applications to access different providers service. This leads to much energy and resource consumption which potentially hinders other common uses of the smartphone. In this paper, we proposed GS1beacon, a GS1 standard based integrated BLE beacon service platform which could find the desired service regardless of providers applications, and provide multiple services in a single BLE beacon. To realize this, we construct a global discovery system by using an Object Name Service (ONS) and unify format of a BLE beacon ID by using a GS1 ID keys. Additionally, we prototyped GS1beacon service platform and showed its feasibility through case study, and performance evaluation.
{"title":"GS1beacon: GS1 Standard Based Integrated Beacon Service Platform","authors":"Wondeuk Yoon, Kiwoong Kwon, Daeyoung Kim","doi":"10.1109/SCC.2016.116","DOIUrl":"https://doi.org/10.1109/SCC.2016.116","url":null,"abstract":"Bluetooth Low Energy beacon service is the newest Online-to-Offline technology. A beacon application in users smartphone connects an offline physical beacon with an online service produced by a service provider. However, since the BLE beacon separately operates by each service provider, users need to run multiple providers applications to access different providers service. This leads to much energy and resource consumption which potentially hinders other common uses of the smartphone. In this paper, we proposed GS1beacon, a GS1 standard based integrated BLE beacon service platform which could find the desired service regardless of providers applications, and provide multiple services in a single BLE beacon. To realize this, we construct a global discovery system by using an Object Name Service (ONS) and unify format of a BLE beacon ID by using a GS1 ID keys. Additionally, we prototyped GS1beacon service platform and showed its feasibility through case study, and performance evaluation.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129549690","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}
N. Zhou, Wesley M. Gifford, Junchi Yan, Hongfei Li
We study a general attrition problem using unsupervised clustering and statistical approaches. The studied problem comes from retention problem in service industries. Our research provides an end-to-end solution from identifying hot job category to analyze the effectiveness of an incentive program applied to the selected categories. One of the barriers of studying the attrition problem is the lack of detailed features of an individual employee due to the confidentiality restriction. Different from the typical attrition approach that requires detailed individual information, we only use the aggregated attrition data and the internal business need data as the base, and cluster the job categories to give a recommendation. We converted the clustering results in a score for the recommendation. To avoid the monthly fluctuation, we apply exponential decay moving average multiple neighboring months on the snapshot scores to ensure consistent recommendation. The end-to-end solution also includes the impact analysis. By comparing the two general groups, we apply an approach similar to A/B test. We score the selected job categories with an effective score. We can apply this research to large consulting/service companies, and government agencies. For those enterprises or institutes, attrition avoidance is a major consideration as their main assets are their top performance employees. There also exist well-defined job roles and skill categories allowing to us to apply this approach.
{"title":"End-to-End Solution with Clustering Method for Attrition Analysis","authors":"N. Zhou, Wesley M. Gifford, Junchi Yan, Hongfei Li","doi":"10.1109/SCC.2016.54","DOIUrl":"https://doi.org/10.1109/SCC.2016.54","url":null,"abstract":"We study a general attrition problem using unsupervised clustering and statistical approaches. The studied problem comes from retention problem in service industries. Our research provides an end-to-end solution from identifying hot job category to analyze the effectiveness of an incentive program applied to the selected categories. One of the barriers of studying the attrition problem is the lack of detailed features of an individual employee due to the confidentiality restriction. Different from the typical attrition approach that requires detailed individual information, we only use the aggregated attrition data and the internal business need data as the base, and cluster the job categories to give a recommendation. We converted the clustering results in a score for the recommendation. To avoid the monthly fluctuation, we apply exponential decay moving average multiple neighboring months on the snapshot scores to ensure consistent recommendation. The end-to-end solution also includes the impact analysis. By comparing the two general groups, we apply an approach similar to A/B test. We score the selected job categories with an effective score. We can apply this research to large consulting/service companies, and government agencies. For those enterprises or institutes, attrition avoidance is a major consideration as their main assets are their top performance employees. There also exist well-defined job roles and skill categories allowing to us to apply this approach.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134584153","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}
Inspired by service computing principles, in cloud manufacturing, manufacturers encapsulate their resources into consumable services that can be looked up and accessed over the Internet. Manufacturing ontologies are used to store the service information. Manufacturers use service rules to control how their resources can be accessed. The rules are normally written in natural language. Thus, they need to be converted to semantic rules that can be understood by the search engine of the manufacturing ontologies. Manually converting service rules to semantic rules is time-consuming and error-prone. This paper proposed an approach that automatically converts service rules to semantic rules. The proposed scheme classifies the semantics of typical service rules into several semantic categories. Natural language processing techniques are used to process the service rules to map the semantic meanings of the rules to the relevant semantic categories. Then, the identified semantic categories are converted to semantic rules. The evaluation of the scheme shows that the scheme achieves good conversion accuracy.
{"title":"Converting Service Rules to Semantic Rules","authors":"Xinfeng Ye, Ping Zhao","doi":"10.1109/SCC.2016.103","DOIUrl":"https://doi.org/10.1109/SCC.2016.103","url":null,"abstract":"Inspired by service computing principles, in cloud manufacturing, manufacturers encapsulate their resources into consumable services that can be looked up and accessed over the Internet. Manufacturing ontologies are used to store the service information. Manufacturers use service rules to control how their resources can be accessed. The rules are normally written in natural language. Thus, they need to be converted to semantic rules that can be understood by the search engine of the manufacturing ontologies. Manually converting service rules to semantic rules is time-consuming and error-prone. This paper proposed an approach that automatically converts service rules to semantic rules. The proposed scheme classifies the semantics of typical service rules into several semantic categories. Natural language processing techniques are used to process the service rules to map the semantic meanings of the rules to the relevant semantic categories. Then, the identified semantic categories are converted to semantic rules. The evaluation of the scheme shows that the scheme achieves good conversion accuracy.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133892264","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}
Honghao Gao, Huai-kou Miao, Yucong Duan, Jinyu Kai
The complex requirements of E-commerce application call for selecting a set of Web services to reuse theirs business logics, where Service Oriented Architecture (SOA) provides a promising solution to the problem of cross-platform services integration. To this purpose, how to discovery services is a key to support the quick services composition, which has been a challenging task in Web application engineering. However, the traditional approaches have limitations in recall ratio and precision ratio because the keyword and semantic annotation query modes are hard to verify the target Web service, such as interaction behaviors and control flows of composite service. In this paper, it proposes a probabilistic model checking based service discovery method. First, it focuses on service reliability and time constraints to formalize the probabilistic behaviors of service process. Second, the quantitative verification properties of user requirements are specified in the form of temporal logic formulae. Third, the service discovery is to verify service process model against expected properties for identifying candidate services. Finally, in order to reduce model checking tasks, the correlation based service recommendation is introduced to explore more suitable services for user. Our framework improves the efficiency of service discovery without changing any existing service infrastructures.
{"title":"Applying Probabilistic Model Checking to Service Discovery Framework","authors":"Honghao Gao, Huai-kou Miao, Yucong Duan, Jinyu Kai","doi":"10.1109/SCC.2016.106","DOIUrl":"https://doi.org/10.1109/SCC.2016.106","url":null,"abstract":"The complex requirements of E-commerce application call for selecting a set of Web services to reuse theirs business logics, where Service Oriented Architecture (SOA) provides a promising solution to the problem of cross-platform services integration. To this purpose, how to discovery services is a key to support the quick services composition, which has been a challenging task in Web application engineering. However, the traditional approaches have limitations in recall ratio and precision ratio because the keyword and semantic annotation query modes are hard to verify the target Web service, such as interaction behaviors and control flows of composite service. In this paper, it proposes a probabilistic model checking based service discovery method. First, it focuses on service reliability and time constraints to formalize the probabilistic behaviors of service process. Second, the quantitative verification properties of user requirements are specified in the form of temporal logic formulae. Third, the service discovery is to verify service process model against expected properties for identifying candidate services. Finally, in order to reduce model checking tasks, the correlation based service recommendation is introduced to explore more suitable services for user. Our framework improves the efficiency of service discovery without changing any existing service infrastructures.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134041758","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}
It is common practice today for small and medium business houses to assemble and host services, than hosting everything themselves. To cater to diverse market needs, these houses often need to subscribe to different services from different information providers. The service contracts and the range of features and facilities supported and provided by the providers vary widely. A non-trivial challenge for a service assembler is in deciding the set of information providers to subscribe to, given the heterogeneity in the offerings provided, the economics of the business model, the target set of customers in the market place and most importantly, the profit margin. We present in this paper, an automated framework that addresses this challenge and aids a service assembler with a cost-feature-performance balanced recommendation of the providers that can best serve his needs. The problem gets exacerbated since there can be multiple dimensions/categories of services (e.g., hotel, flight, and local conveyance in the travel domain) and there can be multiple relevant recommendations which may be of use for the service assemblers. We examine the service subscription recommendation problem from different perspectives and present algorithms for service assembly. Experimental results on small-scale real data as well as large-scale simulation data show the efficacy of our proposal.
{"title":"A Framework for Top Service Subscription Recommendations for Service Assemblers","authors":"S. Chattopadhyay, A. Banerjee, Tridib Mukherjee","doi":"10.1109/SCC.2016.50","DOIUrl":"https://doi.org/10.1109/SCC.2016.50","url":null,"abstract":"It is common practice today for small and medium business houses to assemble and host services, than hosting everything themselves. To cater to diverse market needs, these houses often need to subscribe to different services from different information providers. The service contracts and the range of features and facilities supported and provided by the providers vary widely. A non-trivial challenge for a service assembler is in deciding the set of information providers to subscribe to, given the heterogeneity in the offerings provided, the economics of the business model, the target set of customers in the market place and most importantly, the profit margin. We present in this paper, an automated framework that addresses this challenge and aids a service assembler with a cost-feature-performance balanced recommendation of the providers that can best serve his needs. The problem gets exacerbated since there can be multiple dimensions/categories of services (e.g., hotel, flight, and local conveyance in the travel domain) and there can be multiple relevant recommendations which may be of use for the service assemblers. We examine the service subscription recommendation problem from different perspectives and present algorithms for service assembly. Experimental results on small-scale real data as well as large-scale simulation data show the efficacy of our proposal.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132221080","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}
We present an overall framework of services for indoor navigation, which includes Indoor Mapping, Indoor Positioning, Path Planning, and En-route Assistance. Within such framework we focus on an augmented reality (AR) solution for en-route assistance. AR assists the user walking in a multi-floor building by displaying a directional arrow under a camera view, thus freeing the user from knowing his/her position. Our AR solution relies on geomagnetic positioning and north-oriented space coordinates transformation. Therefore, it can work without infrastructure and without relying on GPS. The AR visual interface and the integration with magnetic positioning is the main novelty of our solution, which has been validated by experiments and shows a good performance.
{"title":"XYZ Indoor Navigation through Augmented Reality: A Research in Progress","authors":"Kaixu Liu, G. Motta, Tianyi Ma","doi":"10.1109/SCC.2016.46","DOIUrl":"https://doi.org/10.1109/SCC.2016.46","url":null,"abstract":"We present an overall framework of services for indoor navigation, which includes Indoor Mapping, Indoor Positioning, Path Planning, and En-route Assistance. Within such framework we focus on an augmented reality (AR) solution for en-route assistance. AR assists the user walking in a multi-floor building by displaying a directional arrow under a camera view, thus freeing the user from knowing his/her position. Our AR solution relies on geomagnetic positioning and north-oriented space coordinates transformation. Therefore, it can work without infrastructure and without relying on GPS. The AR visual interface and the integration with magnetic positioning is the main novelty of our solution, which has been validated by experiments and shows a good performance.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131774161","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}
Rule engines form an essential component of most service execution frameworks in a Service Oriented Architecture (SOA) ecosystem. The efficiency of a service execution framework critically depends on the performance of the rule engine it uses to manage it's operations. Most common rule engines suffer from the fundamental performance issues of the Rete algorithm that they internally use for faster matching of rules against incoming facts. In this paper, we present the design of a scalable architecture of a service rule engine, where a rule clustering and hashing based mechanism is employed for lazy loading of relevant service rules and a prediction based technique for rule evaluation is used for faster actuation of the rules. We present experimental results to demonstrate the efficacy of the proposed rule engine framework over contemporary ones.
{"title":"A Scalable Rule Engine Architecture for Service Execution Frameworks","authors":"S. Chattopadhyay, A. Banerjee, N. Banerjee","doi":"10.1109/SCC.2016.95","DOIUrl":"https://doi.org/10.1109/SCC.2016.95","url":null,"abstract":"Rule engines form an essential component of most service execution frameworks in a Service Oriented Architecture (SOA) ecosystem. The efficiency of a service execution framework critically depends on the performance of the rule engine it uses to manage it's operations. Most common rule engines suffer from the fundamental performance issues of the Rete algorithm that they internally use for faster matching of rules against incoming facts. In this paper, we present the design of a scalable architecture of a service rule engine, where a rule clustering and hashing based mechanism is employed for lazy loading of relevant service rules and a prediction based technique for rule evaluation is used for faster actuation of the rules. We present experimental results to demonstrate the efficacy of the proposed rule engine framework over contemporary ones.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133467029","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 increasing amount of services published on the Web makes it difficult to discover relevant services for users. Unlike the SOAP-based services that are described by structural WSDL documents, RESTful services, the most popular type of services, are mainly described using short texts. The keyword-based discovery technology for RESTful services adopted by existing service registries is insufficient to obtain accurate services according to user requirements. Moreover, it remains a difficult task for users to specify queries that perfectly reflect their requirements due to the lack of knowledge of their expected service functionalities. In this paper, we propose a goal-oriented service discovery approach, which aims to obtain accurate RESTful services for user functional goals. The approach first groups existing services into clusters using topic models. It then clusters the service goals extracted from the textual descriptions of services by leveraging the topic model trained for services. Based on the service goal clusters, our approach can help users refine their initial queries by recommending similar service goals. Finally, relevant services are obtained by matching the service goals selected by users with those of existing services. Experiments conducted on a real-world service dataset crawled from ProgrammableWeb show the effectiveness of the proposed approach.
{"title":"An Approach of Service Discovery Based on Service Goal Clustering","authors":"Neng Zhang, Jian Wang, K. He, Zheng Li","doi":"10.1109/SCC.2016.22","DOIUrl":"https://doi.org/10.1109/SCC.2016.22","url":null,"abstract":"The increasing amount of services published on the Web makes it difficult to discover relevant services for users. Unlike the SOAP-based services that are described by structural WSDL documents, RESTful services, the most popular type of services, are mainly described using short texts. The keyword-based discovery technology for RESTful services adopted by existing service registries is insufficient to obtain accurate services according to user requirements. Moreover, it remains a difficult task for users to specify queries that perfectly reflect their requirements due to the lack of knowledge of their expected service functionalities. In this paper, we propose a goal-oriented service discovery approach, which aims to obtain accurate RESTful services for user functional goals. The approach first groups existing services into clusters using topic models. It then clusters the service goals extracted from the textual descriptions of services by leveraging the topic model trained for services. Based on the service goal clusters, our approach can help users refine their initial queries by recommending similar service goals. Finally, relevant services are obtained by matching the service goals selected by users with those of existing services. Experiments conducted on a real-world service dataset crawled from ProgrammableWeb show the effectiveness of the proposed approach.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133340749","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}
Elli Rapti, A. Karageorgos, C. Houstis, E. Houstis
Traditional service discovery and selection approaches which rely mostly on centralized architectures, have been proven inadequate in the pervasive environment of the Internet of Things (IoT). In such settings, where decentralization of decision-making is mandatory, bio-inspired computing paradigms have emerged due to their inherent capability to operate without any central control. In this paper, taking inspiration from the widely studied bio-inspired Response Threshold Model, a decentralized service discovery and selection model is proposed. Preliminary results indicate that the proposed approach exhibits efficient scalability and routing performance.
{"title":"A Bio-Inspired Service Discovery and Selection Approach for IoT Applications","authors":"Elli Rapti, A. Karageorgos, C. Houstis, E. Houstis","doi":"10.1109/SCC.2016.126","DOIUrl":"https://doi.org/10.1109/SCC.2016.126","url":null,"abstract":"Traditional service discovery and selection approaches which rely mostly on centralized architectures, have been proven inadequate in the pervasive environment of the Internet of Things (IoT). In such settings, where decentralization of decision-making is mandatory, bio-inspired computing paradigms have emerged due to their inherent capability to operate without any central control. In this paper, taking inspiration from the widely studied bio-inspired Response Threshold Model, a decentralized service discovery and selection model is proposed. Preliminary results indicate that the proposed approach exhibits efficient scalability and routing performance.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116640803","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 proliferation of Web services, more and more functionally equivalent services are being published by service providers on the Web. Although more services mean more flexibility for consumers, it also increases the burden of choosing as consumers may have little or no past experience with the service they will interact with. Therefore, reputation systems have been proposed and are playing a crucial role in the service-oriented environment. Current reputation systems are mainly built upon the explicit feedback or rating given by consumers after experiencing the service. Unfortunately, services at the cold-start stage, prior to being rated, face the rating scarcity problem. In this paper, we focus on this problem and address it through a novel reputation model that uses the Elo algorithm to consider consumer-implicit information in a graph analysis approach. A theoretical analysis is conducted to identify the sufficient and necessary condition for the model to converge to a stable state. Furthermore, experiments confirm our model outperforms the widely adopted reputation algorithm in both accuracy and convergence in the situation of rating scarcity.
{"title":"Evaluating Reputation of Web Services under Rating Scarcity","authors":"Xin Zhou, Donghui Lin, T. Ishida","doi":"10.1109/SCC.2016.35","DOIUrl":"https://doi.org/10.1109/SCC.2016.35","url":null,"abstract":"With the proliferation of Web services, more and more functionally equivalent services are being published by service providers on the Web. Although more services mean more flexibility for consumers, it also increases the burden of choosing as consumers may have little or no past experience with the service they will interact with. Therefore, reputation systems have been proposed and are playing a crucial role in the service-oriented environment. Current reputation systems are mainly built upon the explicit feedback or rating given by consumers after experiencing the service. Unfortunately, services at the cold-start stage, prior to being rated, face the rating scarcity problem. In this paper, we focus on this problem and address it through a novel reputation model that uses the Elo algorithm to consider consumer-implicit information in a graph analysis approach. A theoretical analysis is conducted to identify the sufficient and necessary condition for the model to converge to a stable state. Furthermore, experiments confirm our model outperforms the widely adopted reputation algorithm in both accuracy and convergence in the situation of rating scarcity.","PeriodicalId":115693,"journal":{"name":"2016 IEEE International Conference on Services Computing (SCC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777485","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：