2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)最新文献
Pub Date : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00149
Zhaoxin Wu, Yasushi Shinjo
In recent years, webpages are becoming complex rapidly and their loading times are also becoming longer. This paper tackles this problem with personalized edge computing. In typical edge computing, an edge server collaborates with cloud web servers. In personalized edge computing, on the other hand, an edge server called an Edge Server in the Middle (ESM) collaborates with users' mobile devices. Based on personalized edge computing, this paper focuses on two techniques: edge aided caching and edge aided reprioritizing. Edge aided caching reduces the page loading time on mobile devices because an ESM automatically keeps the cached components up to date. Edge aided reprioritizing forces a web browser to show visual components earlier and reduces the white screen time. The ESM also uses HTTP/2 instead of HTTP/1.1. This reduces the number of interactions between a mobile device and the ESM, and makes it possible to use advanced features such as server push and priority. Edge aided caching has been implemented in a PC for the web browser Google Chrome for Android. An experimental result shows that edge aided caching reduced the page loading time of a popular webpage by 59% in a crowded network condition. Another experimental result shows that edge aided reprioritizing reduced the white screen time of a webpage with many photo images by 21%.
{"title":"Improving Web Browsing Experience with Personalized Edge Computing","authors":"Zhaoxin Wu, Yasushi Shinjo","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00149","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00149","url":null,"abstract":"In recent years, webpages are becoming complex rapidly and their loading times are also becoming longer. This paper tackles this problem with personalized edge computing. In typical edge computing, an edge server collaborates with cloud web servers. In personalized edge computing, on the other hand, an edge server called an Edge Server in the Middle (ESM) collaborates with users' mobile devices. Based on personalized edge computing, this paper focuses on two techniques: edge aided caching and edge aided reprioritizing. Edge aided caching reduces the page loading time on mobile devices because an ESM automatically keeps the cached components up to date. Edge aided reprioritizing forces a web browser to show visual components earlier and reduces the white screen time. The ESM also uses HTTP/2 instead of HTTP/1.1. This reduces the number of interactions between a mobile device and the ESM, and makes it possible to use advanced features such as server push and priority. Edge aided caching has been implemented in a PC for the web browser Google Chrome for Android. An experimental result shows that edge aided caching reduced the page loading time of a popular webpage by 59% in a crowded network condition. Another experimental result shows that edge aided reprioritizing reduced the white screen time of a webpage with many photo images by 21%.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132563942","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 : 2019-10-01DOI: 10.1109/iucc/dsci/smartcns.2019.00009
{"title":"Message from the SmartCNS 2019 General Chairs","authors":"","doi":"10.1109/iucc/dsci/smartcns.2019.00009","DOIUrl":"https://doi.org/10.1109/iucc/dsci/smartcns.2019.00009","url":null,"abstract":"","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131647637","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 : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00116
Xiangbin Shi, Kuo Song, Zhaokui Li, Jing Bi, Deyuan Zhang
Hyperspectral image classification has been widely applied in many fields, but it also faces challenges because of small number of labeled samples. In this paper, we propose the Multiscale Multistage Spectral-Spatial Feature Fusion Framework (M^2 S^2 F^2 ) for hyperspectral image classification using small training samples. The Framework is the combination of two deep convolutional neural networks, which can extract more representative and discriminative features by combining the following operations. Firstly, two different scale 3-D cubes are the inputs for the spectral and spatial feature extraction respectively. Secondly, by fusing strong complementary information between different layers, we form multistage spectral and spatial features by fusion primary, intermediate and advanced features of the spectral and spatial features respectively. Spectral and spatial features are extracted by spectral and spatial skipped residual blocks, which can effectively alleviate the problems of gradient degradation. Thirdly, the fusion of complementary multistage spectral and spatial features can improve the classification accuracy. Experimental results on the IN, UP and KSC datasets show the effectiveness of the proposed method using small training samples.
{"title":"M^2 S^2 F^2 : Multiscale Multistage Spectral-Spatial Features Fusion Framework for Hyperspectral Image Classification","authors":"Xiangbin Shi, Kuo Song, Zhaokui Li, Jing Bi, Deyuan Zhang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00116","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00116","url":null,"abstract":"Hyperspectral image classification has been widely applied in many fields, but it also faces challenges because of small number of labeled samples. In this paper, we propose the Multiscale Multistage Spectral-Spatial Feature Fusion Framework (M^2 S^2 F^2 ) for hyperspectral image classification using small training samples. The Framework is the combination of two deep convolutional neural networks, which can extract more representative and discriminative features by combining the following operations. Firstly, two different scale 3-D cubes are the inputs for the spectral and spatial feature extraction respectively. Secondly, by fusing strong complementary information between different layers, we form multistage spectral and spatial features by fusion primary, intermediate and advanced features of the spectral and spatial features respectively. Spectral and spatial features are extracted by spectral and spatial skipped residual blocks, which can effectively alleviate the problems of gradient degradation. Thirdly, the fusion of complementary multistage spectral and spatial features can improve the classification accuracy. Experimental results on the IN, UP and KSC datasets show the effectiveness of the proposed method using small training samples.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301282","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 : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00048
Zechen Zheng, Shaowei Pan, Yichi Zhang
In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution Neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution Neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution Neural Network models are used to predict test sets, and the results show that deeper Convolution Neural Networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.
{"title":"Fruit Tree Disease Recognition Based on Convolutional Neural Networks","authors":"Zechen Zheng, Shaowei Pan, Yichi Zhang","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00048","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00048","url":null,"abstract":"In order to realize the rapid and accurate recognition of fruit tree diseases in orchard environment, this paper puts forward a deep learning model based on Convolution Neural Network to identify fruit tree diseases. In this paper, the data set is processed by the Sobel operator and image enhancement respectively. Then, the network depth, convolution kernel, feature maps, and fully connected layer in the Convolution Neural Network structure use different parameters and softmax classifier. Differently composition networks are used to train processed dataset. Convolution Neural Network models are used to predict test sets, and the results show that deeper Convolution Neural Networks and mean pooling for tiny features in the dataset are more accurate. It can achieve the disease recognition, which includes cab disease, black rot, rust of apple leaves and bacterial spot disease of peach tree leaves. The model has a good recognition function for disease identification of fruit trees and can help real-time monitoring of orchard diseases.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557516","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 : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00114
Zhang Li, Qingsheng Li, Yunqing Guan
This paper presents a Chinese character generation model for cloud information security. The model, which includes the structure and style of Chinese Characters, is defined by the effective Chinese character stroke output method and the Chinese character structure dynamic generation scheme can be used for the information security and protection. Compared with the Chinese character coding system, the description system makes it easier to store the Chinese characters into the web and output in the client in monitoring. It overcomes the shortcomings caused by lack of service information security in entire characters of modern Chinese characters. It provides an effective strategy and method for cloud storage and cloud data security services, and at same time, it also provides a deeper cloud Character information service basis for the system of information in the cloud.
{"title":"A Chinese Character Generation Model for Cloud Information Security","authors":"Zhang Li, Qingsheng Li, Yunqing Guan","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00114","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00114","url":null,"abstract":"This paper presents a Chinese character generation model for cloud information security. The model, which includes the structure and style of Chinese Characters, is defined by the effective Chinese character stroke output method and the Chinese character structure dynamic generation scheme can be used for the information security and protection. Compared with the Chinese character coding system, the description system makes it easier to store the Chinese characters into the web and output in the client in monitoring. It overcomes the shortcomings caused by lack of service information security in entire characters of modern Chinese characters. It provides an effective strategy and method for cloud storage and cloud data security services, and at same time, it also provides a deeper cloud Character information service basis for the system of information in the cloud.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116586104","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 : 2019-10-01DOI: 10.1109/iucc/dsci/smartcns.2019.00007
{"title":"Message from the DSCI 2019 General Chairs","authors":"","doi":"10.1109/iucc/dsci/smartcns.2019.00007","DOIUrl":"https://doi.org/10.1109/iucc/dsci/smartcns.2019.00007","url":null,"abstract":"","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115132351","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 : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00110
Yanpeng Sun, Chenlu Wang, L. Qu
Object detection in images has a wide range of applications in various fields. However, many of the convolutional neural networks recently proposed have higher requirements on computing resources while achieving higher precision, which cannot guarantee good real-time performance on embedded platforms with limited resources. This paper proposed an object detection network suitable for embedded systems. The M-YOLO (Mobile-YOLO) model proposed in this paper combines depthwise separable convolution and residual blocks in feature extraction layers, which helps to reduce the amount of computation of the network. Multi-scale feature fusion is applied to the output layers to improve the accuracy. Experiments show that the M-YOLO model has 9.68M FLOPs (Floating Point Operations), which is about 22% of Tiny-YOLO model. The accuracy of the network reaches 56.61% on the PASCAL VOC dataset, and the speed in ARM is over 3 times faster than Tiny-YOLO model. The network is more suitable for embedded systems.
{"title":"An Object Detection Network for Embedded System","authors":"Yanpeng Sun, Chenlu Wang, L. Qu","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00110","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00110","url":null,"abstract":"Object detection in images has a wide range of applications in various fields. However, many of the convolutional neural networks recently proposed have higher requirements on computing resources while achieving higher precision, which cannot guarantee good real-time performance on embedded platforms with limited resources. This paper proposed an object detection network suitable for embedded systems. The M-YOLO (Mobile-YOLO) model proposed in this paper combines depthwise separable convolution and residual blocks in feature extraction layers, which helps to reduce the amount of computation of the network. Multi-scale feature fusion is applied to the output layers to improve the accuracy. Experiments show that the M-YOLO model has 9.68M FLOPs (Floating Point Operations), which is about 22% of Tiny-YOLO model. The accuracy of the network reaches 56.61% on the PASCAL VOC dataset, and the speed in ARM is over 3 times faster than Tiny-YOLO model. The network is more suitable for embedded systems.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115419161","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 : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00086
Yaofei Chen, Xiaoping Sun, Dejian Liu, S. Li
Aiming at the problem of Unmanned Combat Air Vehicle (UCAV) air combat decision-making and maneuver optimization, an UCAV optimal decision method based on dynamic Bayesian network (DBN) is proposed. Firstly, The DBN maneuver recognition model is established based on the causal relationship between flight characteristic parameters and maneuver actions, and the target flight path is predicted according to the acquired attitude information and trajectory prediction model. Secondly, combined with the comprehensive analysis of other information, the air combat occupation decision is established, and the decision result is the functional index of maneuver optimization to be adopted by UCAV. Finally, used optimal control algorithm to calculate the optimal boot quantity iteratively. The simulation results prove the convergence and real-time performance of the control algorithm, it can meet the requirements of engineering application.
{"title":"Optimal Guidance Method for UCAV in Close Free Air Combat","authors":"Yaofei Chen, Xiaoping Sun, Dejian Liu, S. Li","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00086","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00086","url":null,"abstract":"Aiming at the problem of Unmanned Combat Air Vehicle (UCAV) air combat decision-making and maneuver optimization, an UCAV optimal decision method based on dynamic Bayesian network (DBN) is proposed. Firstly, The DBN maneuver recognition model is established based on the causal relationship between flight characteristic parameters and maneuver actions, and the target flight path is predicted according to the acquired attitude information and trajectory prediction model. Secondly, combined with the comprehensive analysis of other information, the air combat occupation decision is established, and the decision result is the functional index of maneuver optimization to be adopted by UCAV. Finally, used optimal control algorithm to calculate the optimal boot quantity iteratively. The simulation results prove the convergence and real-time performance of the control algorithm, it can meet the requirements of engineering application.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125054745","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}
Recently, graph convolutional neural networks has become a research hotspot for skeleton-based action recognition because of its excellent performance on graph structure data. Compared to traditional methods, it can explicitly exploit the natural connectivity among the joints and improve greater expressive power. In this paper, we propose a two-stream graph convolutional networks with objects for skeleton-based action recognition. An algorithm is designed for matching similar skeleton in adjacent frames, so that we can get the right skeletons which belong to the same person. It performs well when there are other irrelevant persons in the scene. In addition, other features are less employed except for the human joint in skeleton-based action recognition. We introduce limbs orientation information and related objects information. The related objects are treated as joint points which link with hands. The two-stream networks are built to model coordinate features and orientation features respectively, the results of two streams are fused to one. We get good results on the Kinetics dataset with our methods.
{"title":"Graph Convolutional Networks with Objects for Skeleton-Based Action Recognition","authors":"Xiangbin Shi, Haowen Li, Fang Liu, Deyuan Zhang, Jing Bi, Zhaokui Li","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00074","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00074","url":null,"abstract":"Recently, graph convolutional neural networks has become a research hotspot for skeleton-based action recognition because of its excellent performance on graph structure data. Compared to traditional methods, it can explicitly exploit the natural connectivity among the joints and improve greater expressive power. In this paper, we propose a two-stream graph convolutional networks with objects for skeleton-based action recognition. An algorithm is designed for matching similar skeleton in adjacent frames, so that we can get the right skeletons which belong to the same person. It performs well when there are other irrelevant persons in the scene. In addition, other features are less employed except for the human joint in skeleton-based action recognition. We introduce limbs orientation information and related objects information. The related objects are treated as joint points which link with hands. The two-stream networks are built to model coordinate features and orientation features respectively, the results of two streams are fused to one. We get good results on the Kinetics dataset with our methods.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129950507","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 : 2019-10-01DOI: 10.1109/IUCC/DSCI/SmartCNS.2019.00142
Jin Ge, Lexi Xu, Lei Tong, Yuanbing Tian, Xuan Chen, Xiqing Liu, Shiyu Zhou, Shiyu Hu
In recently years, the communication networks envisage the prominent contradiction between the increased requirements for high-quality services and the gradually increased operational problems. However, the existing operation and maintenance face a series of problems: large volume of data, many control links, difficulty of problems localization. We can employ machine to efficiently analyze and deal with these problems. This paper proposes an analysis method for quality of service (QoS)-aware service in the field of operation and maintenance. The proposed method analyzes the correlation between QoS-aware service features and problem solution by mining service scene, operational data, and typical cases. The proposed method is useful for the customer service personnel to locate and solve the problem.
{"title":"Research and Application of Decision Tree Algorithm in QoS-Aware Service for Fault Diagnosis","authors":"Jin Ge, Lexi Xu, Lei Tong, Yuanbing Tian, Xuan Chen, Xiqing Liu, Shiyu Zhou, Shiyu Hu","doi":"10.1109/IUCC/DSCI/SmartCNS.2019.00142","DOIUrl":"https://doi.org/10.1109/IUCC/DSCI/SmartCNS.2019.00142","url":null,"abstract":"In recently years, the communication networks envisage the prominent contradiction between the increased requirements for high-quality services and the gradually increased operational problems. However, the existing operation and maintenance face a series of problems: large volume of data, many control links, difficulty of problems localization. We can employ machine to efficiently analyze and deal with these problems. This paper proposes an analysis method for quality of service (QoS)-aware service in the field of operation and maintenance. The proposed method analyzes the correlation between QoS-aware service features and problem solution by mining service scene, operational data, and typical cases. The proposed method is useful for the customer service personnel to locate and solve the problem.","PeriodicalId":410905,"journal":{"name":"2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127760749","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}
2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)
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