Ming Zhu, Xiukun Yan, Jing Li, Cong Liu, Yawen Cao
Mobile edge computing is playing an increasingly important role in the rise of mobile Internet technology. Services deployed on edge servers nearby mobile users would provide computing capabilities with low latency and high scalability. Usually, a single service is challenging to meet a complex user request, which asks for composing services. With the increasing number of services in the cloud and edge computing environment and the user mobility, selecting appropriate services to meet the complex mobile user’s requests becomes a crucial problem. This paper proposes a modified moth-flame optimization algorithm using overall QoS for service selection. Specifically, the overall QoS of services is calculated by combining the subjective and objective QoS with the ordinal relationship and coefficient of variation, and the moth-flame optimization algorithm is improved by adding the differential evolution algorithm. The experimental results show that the proposed approach outperforms some other services selection approaches.
{"title":"International Journal of Web Services Research (IJWSR): Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm","authors":"Ming Zhu, Xiukun Yan, Jing Li, Cong Liu, Yawen Cao","doi":"10.4018/ijwsr.302888","DOIUrl":"https://doi.org/10.4018/ijwsr.302888","url":null,"abstract":"Mobile edge computing is playing an increasingly important role in the rise of mobile Internet technology. Services deployed on edge servers nearby mobile users would provide computing capabilities with low latency and high scalability. Usually, a single service is challenging to meet a complex user request, which asks for composing services. With the increasing number of services in the cloud and edge computing environment and the user mobility, selecting appropriate services to meet the complex mobile user’s requests becomes a crucial problem. This paper proposes a modified moth-flame optimization algorithm using overall QoS for service selection. Specifically, the overall QoS of services is calculated by combining the subjective and objective QoS with the ordinal relationship and coefficient of variation, and the moth-flame optimization algorithm is improved by adding the differential evolution algorithm. The experimental results show that the proposed approach outperforms some other services selection approaches.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"1 1","pages":"1-23"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89408374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the outbreak of the COVID-19, online diagnosis and treatment services have developed rapidly, but it is not easy for patients to choose the appropriate healthcare service in the face of massive amounts of information. This article proposes a multi-dimensional context-aware healthcare service recommendation method, which consists of a healthcare service matching model and a healthcare service ranking model. The former first collects objective knowledge related to doctors and diseases to build a knowledge graph, then matches a group of healthcare services for patients according to the patient’s input; The latter selects 5 indicators from the doctor’s academic level, geographical location, public influence, reputation, etc. to build a TOPSIS model based on the entropy weight method to recommend the most appropriate healthcare services for patients. Finally, taking the patient in Shiyan as an example, the whole process of the method is demonstrated, and the feasibility of the method is verified.
{"title":"A Multi-Dimensional Context-Aware Healthcare Service Recommendation Method","authors":"Jingbai Tian, Jianghao Yin, Ziqian Mo, Zhong Luo","doi":"10.4018/ijwsr.302658","DOIUrl":"https://doi.org/10.4018/ijwsr.302658","url":null,"abstract":"Due to the outbreak of the COVID-19, online diagnosis and treatment services have developed rapidly, but it is not easy for patients to choose the appropriate healthcare service in the face of massive amounts of information. This article proposes a multi-dimensional context-aware healthcare service recommendation method, which consists of a healthcare service matching model and a healthcare service ranking model. The former first collects objective knowledge related to doctors and diseases to build a knowledge graph, then matches a group of healthcare services for patients according to the patient’s input; The latter selects 5 indicators from the doctor’s academic level, geographical location, public influence, reputation, etc. to build a TOPSIS model based on the entropy weight method to recommend the most appropriate healthcare services for patients. Finally, taking the patient in Shiyan as an example, the whole process of the method is demonstrated, and the feasibility of the method is verified.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"26 1","pages":"1-15"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81861681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhenjun Li, Yinping Liao, Bo Hu, Liangyu Ni, Yunting Lu
Prediction of stock price movement is regarded as a challenging task of financial time series prediction. Due to the complexity and massive financial market data, the research of deep learning approaches for predicting the future price is very difficult. This study attempted to develop a novel framework, named 13f-LSTM, where the AutoRegressive Integrated Moving Average (ARIMA), for the first time, as one of the technical features, Fourier transforms for trend analysis and Long-Short Term Memory (LSTM), including its variants, to forecast the future’s closing prices. Thirteen historical and technical features of stock were selected as inputs of the proposed 13f-LSTM model. Three typical stock market indices in the real world and their corresponding closing prices in 30 trading days are chosen to examine the performance and predictive accuracy of it. The experimental results show that the 13f-LSTM model outperforms other proposed models in both profitability performance and predictive accuracy.
{"title":"A Financial Deep Learning Framework: Predicting the Values of Financial Time Series With ARIMA and LSTM","authors":"Zhenjun Li, Yinping Liao, Bo Hu, Liangyu Ni, Yunting Lu","doi":"10.4018/ijwsr.302640","DOIUrl":"https://doi.org/10.4018/ijwsr.302640","url":null,"abstract":"Prediction of stock price movement is regarded as a challenging task of financial time series prediction. Due to the complexity and massive financial market data, the research of deep learning approaches for predicting the future price is very difficult. This study attempted to develop a novel framework, named 13f-LSTM, where the AutoRegressive Integrated Moving Average (ARIMA), for the first time, as one of the technical features, Fourier transforms for trend analysis and Long-Short Term Memory (LSTM), including its variants, to forecast the future’s closing prices. Thirteen historical and technical features of stock were selected as inputs of the proposed 13f-LSTM model. Three typical stock market indices in the real world and their corresponding closing prices in 30 trading days are chosen to examine the performance and predictive accuracy of it. The experimental results show that the 13f-LSTM model outperforms other proposed models in both profitability performance and predictive accuracy.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"152 1","pages":"1-15"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75999692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Watermark imperceptibility and robustness in the present watermarking algorithm based on discrete wavelet transform (DWT) could be weakened due to data truncation. To solve this problem, a strong robustness watermarking algorithm based on the lifting wavelet transform is proposed. First, the color channels of the original image are separated, and the selected channels are processed through lifting wavelet transform to obtain low-frequency information. The information is then split into blocks, with Hesseneberg decomposition performed on each block. Arnold algorithm is used to scramble the watermark image, and the scrambled watermark is transformed into a binary sequence that is then embedded into the maximum element of Hessenberg decomposed matrix by quantization modulation. The experimental results exhibit a good robustness of this new algorithm in defending against a wide variety of conventional attacks.
{"title":"Strong Robustness Watermarking Algorithm Based on Lifting Wavelet Transform and Hessenberg Decomposition","authors":"Fan Li, Lin Gao, Junfeng Wang, Ruixia Yan","doi":"10.4018/ijwsr.314948","DOIUrl":"https://doi.org/10.4018/ijwsr.314948","url":null,"abstract":"Watermark imperceptibility and robustness in the present watermarking algorithm based on discrete wavelet transform (DWT) could be weakened due to data truncation. To solve this problem, a strong robustness watermarking algorithm based on the lifting wavelet transform is proposed. First, the color channels of the original image are separated, and the selected channels are processed through lifting wavelet transform to obtain low-frequency information. The information is then split into blocks, with Hesseneberg decomposition performed on each block. Arnold algorithm is used to scramble the watermark image, and the scrambled watermark is transformed into a binary sequence that is then embedded into the maximum element of Hessenberg decomposed matrix by quantization modulation. The experimental results exhibit a good robustness of this new algorithm in defending against a wide variety of conventional attacks.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44521161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the rapid development of the mobile internet and the rapid popularization of smart terminal devices, types and content of services are changing with each passing day, these bring serious mobile information overload problems for mobile users. How to provide better service recommendations for users is an urgent problem to be solved. A crowdsourcing service recommendation strategy for mobile scenarios and user trajectory awareness is proposed. First, the location coordinates in the historical log are clustered into regions by clustering algorithms, and then the user's trajectory patterns are mined in different mobile scenarios to extract mobile rules. Furthermore, the mobile rules are extracted and the scenario to which each rule belongs is judged. When performing crowdsourcing service recommendation, the location trajectory and mobile scenario information are perceived in real time, they are used to predict the location area where the user will soon arrive, thereby the crowdsourcing service in the area is pushed to the user.
{"title":"Recommendations for Crowdsourcing Services Based on Mobile Scenarios and User Trajectory Awareness","authors":"Jie Su, Jun Li","doi":"10.4018/ijwsr.299020","DOIUrl":"https://doi.org/10.4018/ijwsr.299020","url":null,"abstract":"With the rapid development of the mobile internet and the rapid popularization of smart terminal devices, types and content of services are changing with each passing day, these bring serious mobile information overload problems for mobile users. How to provide better service recommendations for users is an urgent problem to be solved. A crowdsourcing service recommendation strategy for mobile scenarios and user trajectory awareness is proposed. First, the location coordinates in the historical log are clustered into regions by clustering algorithms, and then the user's trajectory patterns are mined in different mobile scenarios to extract mobile rules. Furthermore, the mobile rules are extracted and the scenario to which each rule belongs is judged. When performing crowdsourcing service recommendation, the location trajectory and mobile scenario information are perceived in real time, they are used to predict the location area where the user will soon arrive, thereby the crowdsourcing service in the area is pushed to the user.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"34 1","pages":"1-18"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85178031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hooria Khan, Ehtesham Zahoor, Sabina Akhtar, O. Perrin
The use of the Cloud computing has been constantly on the rise. However, there are many challenges associated with the Cloud, such as high bandwidth requirements, data security, vendor lock-in and others. The recent rise of decentralized file systems (DFSs) can help mitigate some of these challenges. However, they have some limitations of their own and the current solutions do not provide any mechanism for implementing access control policies. This becomes a hurdle for migrating sensitive data from the Cloud as the associated authorization policies cannot be migrated to the DFSs. In this paper, the authors address the problem of migrating data, and associated authorization policies, from the Cloud to the DFS. They have applied the approach on the content and policies from an actual Cloud provider and it migrates data from AWS S3 to the IPFS and the resource-based authorization policies specified at AWS are added to a custom blockchain solution. The authors have provided implementation details to justify the practicality of the approach.
{"title":"A Blockchain-Based Approach for Secure Data Migration From the Cloud to the Decentralized Storage Systems","authors":"Hooria Khan, Ehtesham Zahoor, Sabina Akhtar, O. Perrin","doi":"10.4018/ijwsr.296688","DOIUrl":"https://doi.org/10.4018/ijwsr.296688","url":null,"abstract":"The use of the Cloud computing has been constantly on the rise. However, there are many challenges associated with the Cloud, such as high bandwidth requirements, data security, vendor lock-in and others. The recent rise of decentralized file systems (DFSs) can help mitigate some of these challenges. However, they have some limitations of their own and the current solutions do not provide any mechanism for implementing access control policies. This becomes a hurdle for migrating sensitive data from the Cloud as the associated authorization policies cannot be migrated to the DFSs. In this paper, the authors address the problem of migrating data, and associated authorization policies, from the Cloud to the DFS. They have applied the approach on the content and policies from an actual Cloud provider and it migrates data from AWS S3 to the IPFS and the resource-based authorization policies specified at AWS are added to a custom blockchain solution. The authors have provided implementation details to justify the practicality of the approach.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"380 1","pages":"1-20"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76760082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In cloud computing, an advanced persistent threat (APT) is a cyber-attack that gains access to a network and remains undetected for some time. As well APTs have proven difficult to detect and protect, in the existing system they fail to analyze the path of an outbreak when the monitor and assign a weight to the nodes. If a path for an outbreak is detected the VM is migrated to hosts that do not account for the overloaded problem and underutilized hosts. In addition to the size of resources occupied by the VM thus here the traffic was increased. This paper proposes the Threat-Path Reckon technique that detects the multiple paths through re-identification and the addition of automatic weight for its neighbor nodes. Based on that weighted paths, the Secured Object Emigration technique invokes a mapping function to migrate the VMs. Finally, the data in the VM are stored in a best-fit distribution, thus it provides security but achieves the search overheads.
{"title":"Threat-Path Estimate-Based Watchword-Chunk Algorithm for Advanced Persistent Threat in the Cloud","authors":"Babu Pandipati, R. P. Sam","doi":"10.4018/ijwsr.299021","DOIUrl":"https://doi.org/10.4018/ijwsr.299021","url":null,"abstract":"In cloud computing, an advanced persistent threat (APT) is a cyber-attack that gains access to a network and remains undetected for some time. As well APTs have proven difficult to detect and protect, in the existing system they fail to analyze the path of an outbreak when the monitor and assign a weight to the nodes. If a path for an outbreak is detected the VM is migrated to hosts that do not account for the overloaded problem and underutilized hosts. In addition to the size of resources occupied by the VM thus here the traffic was increased. This paper proposes the Threat-Path Reckon technique that detects the multiple paths through re-identification and the addition of automatic weight for its neighbor nodes. Based on that weighted paths, the Secured Object Emigration technique invokes a mapping function to migrate the VMs. Finally, the data in the VM are stored in a best-fit distribution, thus it provides security but achieves the search overheads.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"17 1","pages":"1-32"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83587459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/ijwsr.2021100103
Mingjun Xin, Shicheng Chen, Chunjuan Zang
POI recommendation has gradually become an important topic in the field of service recommendation, which is always achieved by mining user behavior patterns. However, the context information of the collaborative signal is not encoded in the embedding process of traditional POI recommendation methods, which is not enough to capture the collaborative signal among different users. Therefore, a POI recommendation algorithm is presented by using social-time context graph neural network model (GNN) in Location-based social networks. First, it finds similarities between different social relationships based on the users' social and temporal behavior. Then, the similarity among different users is calculated by an improved Euclidean distance. Finally, it integrates the graph neural network, the interaction bipartite graph of users and social-time information into the algorithm to generate the final recommendation list in this paper. Experiments on real datasets show that the proposed method is superior to the state-of-the-art POI recommendation methods.
{"title":"A Graph Neural Network-Based Algorithm for Point-of-Interest Recommendation Using Social Relation and Time Series","authors":"Mingjun Xin, Shicheng Chen, Chunjuan Zang","doi":"10.4018/ijwsr.2021100103","DOIUrl":"https://doi.org/10.4018/ijwsr.2021100103","url":null,"abstract":"POI recommendation has gradually become an important topic in the field of service recommendation, which is always achieved by mining user behavior patterns. However, the context information of the collaborative signal is not encoded in the embedding process of traditional POI recommendation methods, which is not enough to capture the collaborative signal among different users. Therefore, a POI recommendation algorithm is presented by using social-time context graph neural network model (GNN) in Location-based social networks. First, it finds similarities between different social relationships based on the users' social and temporal behavior. Then, the similarity among different users is calculated by an improved Euclidean distance. Finally, it integrates the graph neural network, the interaction bipartite graph of users and social-time information into the algorithm to generate the final recommendation list in this paper. Experiments on real datasets show that the proposed method is superior to the state-of-the-art POI recommendation methods.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"1 1","pages":"51-74"},"PeriodicalIF":1.1,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89830352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/ijwsr.2021100102
Geeta Rani, V. Dhaka, Sonam, U. Pandey, P. Tiwari
In this manuscript, an Intelligent and Adaptive Web Page Recommender System is proposed that provides personalized, global and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: Uniformity and Recommendation strength. The system continuously tracks the user’s responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset which is a significant improvement over the 70% F1 measure reported by Automatic Clustering-based Genetic Algorithm, the prior web recommender system.
{"title":"Intelligent and Adaptive Web Page Recommender System","authors":"Geeta Rani, V. Dhaka, Sonam, U. Pandey, P. Tiwari","doi":"10.4018/ijwsr.2021100102","DOIUrl":"https://doi.org/10.4018/ijwsr.2021100102","url":null,"abstract":"In this manuscript, an Intelligent and Adaptive Web Page Recommender System is proposed that provides personalized, global and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: Uniformity and Recommendation strength. The system continuously tracks the user’s responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset which is a significant improvement over the 70% F1 measure reported by Automatic Clustering-based Genetic Algorithm, the prior web recommender system.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"13 1","pages":"27-50"},"PeriodicalIF":1.1,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75818949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.4018/ijwsr.2021100104
N. Thilagavathi, K. Lakshmi
To verify the composed Web services, a general view of what traits of a service need to be identified is still lacking. The existing verification model did not address any mechanism for getting alternative services if we failed to reach the desired service and partially concentrated on the reachability problem for a deterministic and non-deterministic system in sequential. This paper proposes a Synthesised Non-deterministic Turing Machine Model (SNTMM) by combining the Multistacked Non-deterministic Turing Machine (MSNTM) model and Multitaped Non-deterministic Turing Machine (MTNTM) model to verify the composed Web services for both deterministic and non-deterministic systems in parallel. The deceased transition and departed service marking algorithm have been proposed to address each participated service’s reachability in composing service for all possible input in parallel. This article shows an example to demonstrate the meticulousness of the model. The experimental results show that the performance of the proposed model is measured efficiently
{"title":"Verification of Composed Web Service Using Synthesized Nondeterministic Turing Model (SNTMM) With Multiple Tapes and Stacks","authors":"N. Thilagavathi, K. Lakshmi","doi":"10.4018/ijwsr.2021100104","DOIUrl":"https://doi.org/10.4018/ijwsr.2021100104","url":null,"abstract":"To verify the composed Web services, a general view of what traits of a service need to be identified is still lacking. The existing verification model did not address any mechanism for getting alternative services if we failed to reach the desired service and partially concentrated on the reachability problem for a deterministic and non-deterministic system in sequential. This paper proposes a Synthesised Non-deterministic Turing Machine Model (SNTMM) by combining the Multistacked Non-deterministic Turing Machine (MSNTM) model and Multitaped Non-deterministic Turing Machine (MTNTM) model to verify the composed Web services for both deterministic and non-deterministic systems in parallel. The deceased transition and departed service marking algorithm have been proposed to address each participated service’s reachability in composing service for all possible input in parallel. This article shows an example to demonstrate the meticulousness of the model. The experimental results show that the performance of the proposed model is measured efficiently","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"25 1","pages":"75-102"},"PeriodicalIF":1.1,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87324897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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