Low Choon Keat, T. F. Ang, Chun Yong Chong, Y. Tew
Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.
{"title":"(Offloading) QoE-Aware Application Mapping and Energy-Aware Module Placement in Fog Computing + Offloading","authors":"Low Choon Keat, T. F. Ang, Chun Yong Chong, Y. Tew","doi":"10.4018/ijwsr.299017","DOIUrl":"https://doi.org/10.4018/ijwsr.299017","url":null,"abstract":"Fog computing is a potential solution for the Internet of Things in close connection with things and end-users. Fog computing will easily transfer sensitive data without delaying distributed devices. Moreover, fog computing is more in real-time streaming applications, sensor networks, IoT which need high speed and reliable internet connectivity. Due to the heterogeneous and distributed characteristics, finley distributing the task with computation offloading is a challenging task. Developing an efficient QoE-aware application mapping policy is challenging due to the different user interests. The energy consumption would usually increase after such an algorithm and policy are implemented. In this paper, we enhanced the future from the previous QoE paper by proposing a computation offloading algorithm. The proposed algorithm is to prevent overloading on fog devices. Our proposed solution has been evaluated and compared with other existing solutions, the results show that our proposed solution performs better in terms of execution time, energy consumption, and network usage.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"113 1","pages":"1-28"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87063210","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}
The mobile edge computing (MEC) model is featured by the ability to provision elastic computing resources close to user requests at the edge of the internet. This paradigm moves traditional digital infrastructure close to mobile networks and extensively reduces application latency for mobile computing tasks like online gaming and video streaming. Nevertheless, it remains a difficulty to provide a effective and performance-guaranteed edge service offloading and migration in the MEC environment. Most existing contributions in this area consider task offloading as a offline decision making process by exploiting transient positions of mobile requesters as model inputs. In this work instead, we develop a predictive-trajectory-aware and online MEC task offloading strategy. Simulations based on real-world MEC deployment datasets and a campus mobile trajectory datasets clearly illustrate that our approach outperforms state-of-the-art ones in terms of effective service rate and migration overhead.
{"title":"A Predictive and Trajectory-Aware Edge Service Allocation Approach in a Mobile Computing Environment","authors":"Ling Huang, B. Shuai","doi":"10.4018/ijwsr.302639","DOIUrl":"https://doi.org/10.4018/ijwsr.302639","url":null,"abstract":"The mobile edge computing (MEC) model is featured by the ability to provision elastic computing resources close to user requests at the edge of the internet. This paradigm moves traditional digital infrastructure close to mobile networks and extensively reduces application latency for mobile computing tasks like online gaming and video streaming. Nevertheless, it remains a difficulty to provide a effective and performance-guaranteed edge service offloading and migration in the MEC environment. Most existing contributions in this area consider task offloading as a offline decision making process by exploiting transient positions of mobile requesters as model inputs. In this work instead, we develop a predictive-trajectory-aware and online MEC task offloading strategy. Simulations based on real-world MEC deployment datasets and a campus mobile trajectory datasets clearly illustrate that our approach outperforms state-of-the-art ones in terms of effective service rate and migration overhead.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"22 4 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":"90394629","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}
Takahide Matsutsuka, Masatoshi Ogawa, Yohei Toriyama, Noriyasu Aso, I. Iida
In order to enhance the customer experience, it is important not only to provide functions, but also to respond to changes in environments and requirements. It is a difficult task to evaluate and manage which function with different locations and contents is most valuable to the user's experience without using computationally time-consuming optimization calculations. To address this, this paper is focusing on self-adaptive software technology. The authors built a software adaptation mechanism that can be immediately calculated online using a performance characteristic map and threshold judgment with a learning function and sequential updates. The results confirmed the effectiveness of the mechanism in an application that supports personnel exchange events.
{"title":"An Adaptive System for a Real-Time Matching Application","authors":"Takahide Matsutsuka, Masatoshi Ogawa, Yohei Toriyama, Noriyasu Aso, I. Iida","doi":"10.4018/ijwsr.299018","DOIUrl":"https://doi.org/10.4018/ijwsr.299018","url":null,"abstract":"In order to enhance the customer experience, it is important not only to provide functions, but also to respond to changes in environments and requirements. It is a difficult task to evaluate and manage which function with different locations and contents is most valuable to the user's experience without using computationally time-consuming optimization calculations. To address this, this paper is focusing on self-adaptive software technology. The authors built a software adaptation mechanism that can be immediately calculated online using a performance characteristic map and threshold judgment with a learning function and sequential updates. The results confirmed the effectiveness of the mechanism in an application that supports personnel exchange events.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"27 1","pages":"1-22"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78447643","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}
The process of intrusion detection usually involves identifying complex intrusion signatures from a huge repository. This requires a complex model that can identify these signatures. This work presents a deep learning based neural network model that can perform effective intrusion detection on network transmission data. The proposed multi-layered deep learning network is composed of multiple hidden processing layers in the network that makes it a deep learning network. Detection using the deep network was observed to exhibit effective performances in detecting the intrusion signatures. Experiments were performed on standard benchmark datasets like KDD CUP 99, NSL-KDD, and Koyoto 2006+ datasets. Comparisons were performed with state-of-the-art models in literature, and the results and comparisons indicate high performances by the proposed algorithm.
入侵检测过程通常涉及从庞大的存储库中识别复杂的入侵特征。这需要一个能够识别这些签名的复杂模型。本文提出了一种基于深度学习的神经网络模型，该模型可以对网络传输数据进行有效的入侵检测。所提出的多层深度学习网络由网络中的多个隐藏处理层组成，使其成为深度学习网络。使用深度网络的检测在检测入侵特征方面表现出有效的性能。实验在标准基准数据集上进行，如KDD CUP 99、NSL-KDD和Koyoto 2006+数据集。与文献中最先进的模型进行了比较，结果和比较表明所提出的算法具有较高的性能。
{"title":"Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks","authors":"P. Chellammal, P. D. Sheba, K. Reka, G. Raja","doi":"10.4018/ijwsr.310057","DOIUrl":"https://doi.org/10.4018/ijwsr.310057","url":null,"abstract":"The process of intrusion detection usually involves identifying complex intrusion signatures from a huge repository. This requires a complex model that can identify these signatures. This work presents a deep learning based neural network model that can perform effective intrusion detection on network transmission data. The proposed multi-layered deep learning network is composed of multiple hidden processing layers in the network that makes it a deep learning network. Detection using the deep network was observed to exhibit effective performances in detecting the intrusion signatures. Experiments were performed on standard benchmark datasets like KDD CUP 99, NSL-KDD, and Koyoto 2006+ datasets. Comparisons were performed with state-of-the-art models in literature, and the results and comparisons indicate high performances by the proposed algorithm.","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":"48739060","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}
The map matching method gets simpler with higher precision positioning systems, but because the positioning framework is still not sufficiently precise or too costly for marginal map matching in practice, it is still a hot research domain. Several researchers have worked on map-matching methods and reported their finding of in-depth studies of domain. This literature review provides extensive information on the above map-matching methods related to digital maps with respect to convergence and outline the problems, information sources, as well as future demands identified by industry/society. It focuses on past research work approaches, implementations, capabilities, and their weaknesses using linear search and citation chaining. Finally, this work concludes with recommendations of the future direction of research and ideas to develop new algorithms for advanced applications.
{"title":"A Comprehensive Review of Map-Matching Techniques","authors":"AJAY KUMAR GUPTA, Udai Shanker","doi":"10.4018/ijwsr.306243","DOIUrl":"https://doi.org/10.4018/ijwsr.306243","url":null,"abstract":"The map matching method gets simpler with higher precision positioning systems, but because the positioning framework is still not sufficiently precise or too costly for marginal map matching in practice, it is still a hot research domain. Several researchers have worked on map-matching methods and reported their finding of in-depth studies of domain. This literature review provides extensive information on the above map-matching methods related to digital maps with respect to convergence and outline the problems, information sources, as well as future demands identified by industry/society. It focuses on past research work approaches, implementations, capabilities, and their weaknesses using linear search and citation chaining. Finally, this work concludes with recommendations of the future direction of research and ideas to develop new algorithms for advanced applications.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43834429","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 increasing amount of medical data and the high dimensional and diversified complex information, based on artificial intelligence and machine learning, a new way is provided that is multi-source, heterogeneous, high dimensional, real-time, multi-scale, dynamic, and uncertain. Driven by medical and health big data and using deep learning theories and methods, this paper proposes a new mode of “multi-modal fusion-association mining-analysis and prediction-intelligent decision” for intelligent medicine analysis and decision making. First, research on “multi-modal fusion method of medical big data based on deep learning” explores a new method of medical big data fusion in complex environment. Second, research on “dynamic change rules and analysis and prediction methods of medical big data based on deep learning” explores a new method for medical big data fusion in complex environment. Third, research on “intelligent medicine decision method” explores a new intelligent medicine decision method.
{"title":"Research on Intelligent Medical Engineering Analysis and Decision Based on Deep Learning","authors":"Bao Juan, Tuo Min, Hou Meng Ting, L. Yu, Wang Qun","doi":"10.4018/ijwsr.314949","DOIUrl":"https://doi.org/10.4018/ijwsr.314949","url":null,"abstract":"With the increasing amount of medical data and the high dimensional and diversified complex information, based on artificial intelligence and machine learning, a new way is provided that is multi-source, heterogeneous, high dimensional, real-time, multi-scale, dynamic, and uncertain. Driven by medical and health big data and using deep learning theories and methods, this paper proposes a new mode of “multi-modal fusion-association mining-analysis and prediction-intelligent decision” for intelligent medicine analysis and decision making. First, research on “multi-modal fusion method of medical big data based on deep learning” explores a new method of medical big data fusion in complex environment. Second, research on “dynamic change rules and analysis and prediction methods of medical big data based on deep learning” explores a new method for medical big data fusion in complex environment. Third, research on “intelligent medicine decision method” explores a new intelligent medicine decision method.","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":"48654222","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}
This paper proposes an effective and optimal sentiment classification method named Penguin Rider optimization algorithm-based Deep Recurrent Neural Network (PeROA-based Deep RNN) to perform sentiment classification using political reviews. However, the proposed PeROA is developed by incorporating the Penguins Search Optimization Algorithm (PeSOA) with the Rider Optimization Algorithm (ROA). The sentiment classification process is progressed using the Deep RNN classifier, which in turn generate the optimal solution based on the fitness measure. Accordingly, the function with the minimal error value is accepted as the best solution. The sentiment-based features enable the classifier to perform better classification result with respect to the sentiment tweets. However, the proposed PeROA-based Deep RNN obtained better performance using the metrics, like accuracy, sensitivity, specificity, recall, F-measure, thread score, NPV, FPR,FNR and FDR with the values of 92.030%, 92.030%, 92.235%, 92.030%, 92.030%, 92.030%, 92.030%, 3.105%, 3.11%, and 3.105%, respectively.
本文提出了一种有效且最优的情感分类方法——基于企鹅骑手优化算法的深度递归神经网络(PeROA-based Deep RNN)，利用政治评论进行情感分类。该算法将企鹅搜索优化算法(PeSOA)与骑手优化算法(ROA)相结合。使用深度RNN分类器进行情感分类过程，然后根据适应度度量生成最优解。因此，接受误差值最小的函数作为最佳解。基于情感的特征使分类器能够对情感推文执行更好的分类结果。然而，基于peroa的深度RNN在准确率、灵敏度、特异性、召回率、F-measure、线程得分、NPV、FPR、FNR和FDR等指标上表现更好，分别为92.030%、92.030%、92.235%、92.030%、92.030%、92.030%、3.105%、3.11%和3.105%。
{"title":"Penguin Rider Optimization Algorithm-Based Deep Recurrent Neural Network for Sentiment Classification of Political Twitter Data","authors":"Vegi Harendranath, S. Rodda","doi":"10.4018/ijwsr.299019","DOIUrl":"https://doi.org/10.4018/ijwsr.299019","url":null,"abstract":"This paper proposes an effective and optimal sentiment classification method named Penguin Rider optimization algorithm-based Deep Recurrent Neural Network (PeROA-based Deep RNN) to perform sentiment classification using political reviews. However, the proposed PeROA is developed by incorporating the Penguins Search Optimization Algorithm (PeSOA) with the Rider Optimization Algorithm (ROA). The sentiment classification process is progressed using the Deep RNN classifier, which in turn generate the optimal solution based on the fitness measure. Accordingly, the function with the minimal error value is accepted as the best solution. The sentiment-based features enable the classifier to perform better classification result with respect to the sentiment tweets. However, the proposed PeROA-based Deep RNN obtained better performance using the metrics, like accuracy, sensitivity, specificity, recall, F-measure, thread score, NPV, FPR,FNR and FDR with the values of 92.030%, 92.030%, 92.235%, 92.030%, 92.030%, 92.030%, 92.030%, 3.105%, 3.11%, and 3.105%, respectively.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"36 1","pages":"1-25"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79196551","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}
Yong Ma, Yangguo Liu, Shiyun Shao, Jiale Zhao, Jun Tang
Parking space detection is an important part of the automatic parking assistance system. How to use existing sensors to accurately and effectively detect parking spaces is the key problem that has not been solved in the automatic parking system. Advances in Artificial Intelligence and sensing technologies have motivated significant research and development in parking space detection in the automotive field. Firstly, based on extensive investigation of a lot of literature and the latest re-search results, this paper divides parking space detection methods into methods based on traditional visual features and those methods based on deep learning and introduces them separately. Secondly, the advantages and disadvantages of each parking space detection method are analyzed, compared, and summarized. And the benchmark datasets and algorithm evaluation standards commonly used in parking space detection methods are introduced. Finally, the vision-based parking space detection method is summarized, and the future development trend is prospected.
{"title":"Review of Research on Vision-Based Parking Space Detection Method","authors":"Yong Ma, Yangguo Liu, Shiyun Shao, Jiale Zhao, Jun Tang","doi":"10.4018/ijwsr.304061","DOIUrl":"https://doi.org/10.4018/ijwsr.304061","url":null,"abstract":"Parking space detection is an important part of the automatic parking assistance system. How to use existing sensors to accurately and effectively detect parking spaces is the key problem that has not been solved in the automatic parking system. Advances in Artificial Intelligence and sensing technologies have motivated significant research and development in parking space detection in the automotive field. Firstly, based on extensive investigation of a lot of literature and the latest re-search results, this paper divides parking space detection methods into methods based on traditional visual features and those methods based on deep learning and introduces them separately. Secondly, the advantages and disadvantages of each parking space detection method are analyzed, compared, and summarized. And the benchmark datasets and algorithm evaluation standards commonly used in parking space detection methods are introduced. Finally, the vision-based parking space detection method is summarized, and the future development trend is prospected.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"15 1","pages":"1-25"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91304648","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}
Task-oriented dialogue systems aim to engage in interactive dialogue with people to ultimately complete specific tasks. Typical application domains include ticket booking, online shopping, and healthcare providing. Medical dialogue systems can interact with patients, provide initial clinical advice, and improve the efficiency and quality of healthcare services. However, current medical dialogue systems lack the ability to utilize domain knowledge. This paper extracts regular domain knowledge as well as medical process knowledge from clinical guidelines to improve the performance of dialogue systems. Regular knowledge is used to generate accurate responses for a given input, and process knowledge is used to steer the conversation. The authors divide the task of multi-turn conversation generation into four sub-tasks and propose a 4-layer knowledge-based process-aware dialogue model that incorporates the domain knowledge to generate responses. Results indicate that the approach can lead medical conversations actively while providing accurate responses.
{"title":"Process-Aware Dialogue System With Clinical Guideline Knowledge","authors":"Meng Wang, Feng Gao, J. Gu","doi":"10.4018/ijwsr.304392","DOIUrl":"https://doi.org/10.4018/ijwsr.304392","url":null,"abstract":"Task-oriented dialogue systems aim to engage in interactive dialogue with people to ultimately complete specific tasks. Typical application domains include ticket booking, online shopping, and healthcare providing. Medical dialogue systems can interact with patients, provide initial clinical advice, and improve the efficiency and quality of healthcare services. However, current medical dialogue systems lack the ability to utilize domain knowledge. This paper extracts regular domain knowledge as well as medical process knowledge from clinical guidelines to improve the performance of dialogue systems. Regular knowledge is used to generate accurate responses for a given input, and process knowledge is used to steer the conversation. The authors divide the task of multi-turn conversation generation into four sub-tasks and propose a 4-layer knowledge-based process-aware dialogue model that incorporates the domain knowledge to generate responses. Results indicate that the approach can lead medical conversations actively while providing accurate responses.","PeriodicalId":54936,"journal":{"name":"International Journal of Web Services Research","volume":"26 1","pages":"1-22"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87959705","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}
The tracking of spatial objects in indoor location-based services is becoming increasingly important for many applications. However, much research has focused only on querying and indexing in indoor spaces without considering the indoor variations. Therefore, this paper presents an indoor framework which includes data structures of indoor environments comprised of various building features and multiple floors. Moreover, the indoor framework includes indoor navigation and routing for both directed and undirected indoor environments, indoor density which takes into account the room capacity, and movement trajectories in single and multi-floor structures. Using synthetic data, the authors conducted extensive experiments to evaluate the proposed framework. The results show that this indoor framework can be implemented efficiently and effectively.
{"title":"Indoor Framework","authors":"S. Alamri","doi":"10.4018/ijwsr.314630","DOIUrl":"https://doi.org/10.4018/ijwsr.314630","url":null,"abstract":"The tracking of spatial objects in indoor location-based services is becoming increasingly important for many applications. However, much research has focused only on querying and indexing in indoor spaces without considering the indoor variations. Therefore, this paper presents an indoor framework which includes data structures of indoor environments comprised of various building features and multiple floors. Moreover, the indoor framework includes indoor navigation and routing for both directed and undirected indoor environments, indoor density which takes into account the room capacity, and movement trajectories in single and multi-floor structures. Using synthetic data, the authors conducted extensive experiments to evaluate the proposed framework. The results show that this indoor framework can be implemented efficiently and effectively.","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":"44190365","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}
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：