2020 IEEE International Conference on Smart Data Services (SMDS)最新文献

Machine Learning based User QoE Evaluation for Video Streaming over Mobile Network 基于机器学习的移动网络视频流用户QoE评价

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/SMDS49396.2020.00010

Yanhong Zhu, Tao Sun, Qin Li, Lu Lu, Xiaodong Duan, Weiyuan Li

{"title":"Machine Learning based User QoE Evaluation for Video Streaming over Mobile Network","authors":"Yanhong Zhu, Tao Sun, Qin Li, Lu Lu, Xiaodong Duan, Weiyuan Li","doi":"10.1109/SMDS49396.2020.00010","DOIUrl":"https://doi.org/10.1109/SMDS49396.2020.00010","url":null,"abstract":"Quality of experience (QoE) serves as a direct evaluation of users' experience in mobile video transmission and is critical to ensure good network service. Although many efforts have been made to predict QoE based on network parameters of the user terminal equipment, it is difficult to predict QoE based on Quality of Service (QoS) offered by the network servers. In this paper, a machine learning based QoE evaluation method is proposed to evaluate user QoE in real-time by analyzing the QoS characteristics for mobile video transmission. For this purpose, we construct a large-scale dataset by collecting more than 300 thousand pieces of metrics data with two kinds of key quality indicators (KQIs) describing the QoE and 91 key performance indicators (KPIs) describing the QoS. A two-process feature subset selection (FSS) method consisting of single parameter pre-FSS and multi-parameter FSS is then proposed to find the KPIs related to KQIs. An Extra-Trees model is finally developed to learn the relationships between the KPIs and KQIs. By employing machine learning and data analytics on network data with the data-driven framework, the proposed method can predict the user QoE according to the QoS of network servers. The results prove that our proposed method can outperform other state-of-the-art approaches.","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129777692","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}

引用次数: 1

2020 IEEE International Conference on Smart Data Services (SMDS) SMDS 2020 2020 IEEE智能数据服务国际会议(SMDS

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/smds49396.2020.00004

Lixin Gao, Guang Cheng, M. Sapino

引用次数: 0

2020 IEEE International Conference on Smart Data Services 2020 IEEE智能数据服务国际会议

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/smds49396.2020.00002

引用次数: 0

Geolocation using GAT with Multiview Learning 使用GAT与多视图学习的地理定位

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/SMDS49396.2020.00017

Zhan Wang, Chunyang Ye, Hui Zhou

引用次数: 0

2020 IEEE International Conference on Smart Data Services SMDS 2020 2020 IEEE智能数据服务国际会议SMDS 2020

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/smds49396.2020.00001

引用次数: 0

Utber: Utilizing Fine-Grained Entity Types to Relation Extraction with Distant Supervision 利用细粒度实体类型进行远程监督的关系提取

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/SMDS49396.2020.00015

Chengmin Wu, Lei Chen

{"title":"Utber: Utilizing Fine-Grained Entity Types to Relation Extraction with Distant Supervision","authors":"Chengmin Wu, Lei Chen","doi":"10.1109/SMDS49396.2020.00015","DOIUrl":"https://doi.org/10.1109/SMDS49396.2020.00015","url":null,"abstract":"Recently, much effort has been paid to relation extraction during the construction of large ontological knowledge bases (KBs). However, most of the traditional relation extraction systems rely on human-annotated data for training, which requires expensive human effort. Therefore, Distant supervision is proposed to assist the creation of large amounts of labeled data. By this method, an existing KB is heuristically aligned to texts, and the alignment data are treated as training data. Nevertheless, the noise in the training data may cause two serious problems. First, the heuristic label alignment may fail and cause the wrong label problem. Second, the existing statistical models are applied to ad-hoc features, and hence perform poorly due to the dynamic features of noisy data. To address these two problems, in this paper, we propose a novel framework for automatic relation extraction from unstructured text corpora. Specifically, to solve the first problem, we propose a fine-grained entity typing technique to filter wrong data by choosing positive entity type pairs and conduct joint instance-type selection over bag of instances. To solve the second problem, instead of directly defining manually crafted features, we propose a deep neural architecture with attention mechanism to automatically learn positive and negative instance features. Extensive experiments on real-world datasets demonstrate that our method outperforms the competitive state-of-the-art techniques in terms of effectiveness.","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124009023","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}

引用次数: 1

MY-AIR: A Personalized Air-quality Information Service MY-AIR:个性化空气质量信息服务

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/SMDS49396.2020.00020

Jane Lin, Ouri E. Wolfson

引用次数: 0

Message from the Program Chairs in Chief 来自项目主席的信息

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/COMPSAC.2016.14

H. Sato, M. Matskin, W. Claycomb

引用次数: 0

Copyright 版权

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/smds49396.2020.00003

引用次数: 0

Scalable and Hybrid Ensemble-Based Causality Discovery 基于可扩展和混合集成的因果关系发现

2020 IEEE International Conference on Smart Data Services (SMDS) Pub Date : 2020-10-01 DOI: 10.1109/SMDS49396.2020.00016

Pei Guo, Achuna Ofonedu, Jianwu Wang

{"title":"Scalable and Hybrid Ensemble-Based Causality Discovery","authors":"Pei Guo, Achuna Ofonedu, Jianwu Wang","doi":"10.1109/SMDS49396.2020.00016","DOIUrl":"https://doi.org/10.1109/SMDS49396.2020.00016","url":null,"abstract":"Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-driven causality discovery methods, e.g., Granger causality, PCMCI and Dynamic Bayesian Network, have been proposed. Many of these causality discovery approaches mine time series data and generate a directed causality graph where each graph edge denotes a cause-effect relationship between the two connected graph nodes. Our benchmarking of different causality discovery approaches with real-world climate data shows these approaches often generate quite different causality results with the same input dataset due to their internal learning mechanism differences. Meanwhile, there are ever-increasing available data in virtually every discipline, which makes it more and more difficult to use existing causality discovery algorithms to produce causality results within reasonable time. To address these two challenges, this paper utilizes data partitioning and ensemble techniques, and proposes a two-phase hybrid causality ensemble framework. The framework first conducts phase 1 data ensemble for partitioned data and then conducts phase 2 algorithm ensemble from data ensemble results. To achieve scalability, we further parallelize the ensemble approaches via the Spark big data analytics engine. Our experiments show that our proposed approaches achieve good accuracy through ensemble and high scalability through data-parallelization in distributed computing environments.","PeriodicalId":385149,"journal":{"name":"2020 IEEE International Conference on Smart Data Services (SMDS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121495453","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}

引用次数: 5