2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第4页

Realtime Traffic Speed Estimation with Sparse Crowdsourced Data 基于稀疏众包数据的实时交通速度估计

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00038

Zheng Liu, Lei Chen, Yongxin Tong

{"title":"Realtime Traffic Speed Estimation with Sparse Crowdsourced Data","authors":"Zheng Liu, Lei Chen, Yongxin Tong","doi":"10.1109/ICDE.2018.00038","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00038","url":null,"abstract":"Realtime traffic speed estimation is an important issue in urban computation. Existing approaches usually focus on exploiting the periodicity properties of the traffic speed and utilize crowdsourcing techniques to facilitate real-time estimation. The quality of such estimation is limited in real world: 1) the accuracy of existing estimation over-relies on the probed data; 2) the accidental traffic variance is ignored; 3) existing strategies incur exhaustive usage of human workers to get fine-grained estimation results. Thus, a more intelligent RTSE approach is desired. In this paper, we propose the framework of CrowdRTSE (Crowdsourcing-based Real-time Traffic Speed Estimation), which adopts a hybrid offline-online process to collaboratively exploit the historical and real-time data to produce high-quality RTSE. To accomplish such a framework, we devise effective algorithms to judiciously select the best group of human workers with a constant approximation ratio, and effectively propagate the crowdsourced data with high efficiency. Comprehensive evaluations have been conducted on both synthetic and real world datasets. The experimental results verify the effectiveness and efficiency of our proposed methods.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132367557","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: 11

DisTenC: A Distributed Algorithm for Scalable Tensor Completion on Spark DisTenC:一个基于Spark的分布式可伸缩张量补全算法

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00022

Hancheng Ge, Kai Zhang, Majid Alfifi, Xia Hu, James Caverlee

{"title":"DisTenC: A Distributed Algorithm for Scalable Tensor Completion on Spark","authors":"Hancheng Ge, Kai Zhang, Majid Alfifi, Xia Hu, James Caverlee","doi":"10.1109/ICDE.2018.00022","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00022","url":null,"abstract":"How can we efficiently recover missing values for very large-scale real-world datasets that are multi-dimensional even when the auxiliary information is regularized at certain mode? Tensor completion is a useful tool to recover a low-rank tensor that best approximates partially observed data and further predicts the unobserved data by this low-rank tensor, which has been successfully used for many applications such as location-based recommender systems, link prediction, targeted advertising, social media search, and event detection. Due to the curse of dimensionality, existing algorithms for tensor completion that integrate auxiliary information do not scale for tensors with billions of elements. In this paper, we propose DisTenC, a new distributed large-scale tensor completion algorithm that can be distributed on Spark. Our key insights are to (i) efficiently handle trace-based regularization terms; (ii) update factor matrices with caching; and (iii) optimize the update of the new tensor via residuals. In this way, we can tackle the high computational costs of traditional approaches and minimize intermediate data, leading to order-of-magnitude improvements in tensor completion. Experimental results demonstrate that DisTenC is capable of handling up to 10~1000X larger tensors than existing methods with much faster convergence rate, shows better linearity on machine scalability, and achieves up to an average improvement of 23.5% in accuracy in applications.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122741376","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: 14

An NVM Carol: Visions of NVM Past, Present, and Future NVM卡罗尔:NVM的过去，现在和未来的愿景

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00011

M. Seltzer, Virendra J. Marathe, Steve Byan

引用次数: 11

A Graph-Theoretic Fusion Framework for Unsupervised Entity Resolution 无监督实体解析的图论融合框架

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00070

Dongxiang Zhang, Long Guo, Xiangnan He, Jie Shao, Sai Wu, Heng Tao Shen

引用次数: 17

iZone: Efficient Influence Zone Evaluation over Geo-Textual Data 基于地理文本数据的有效影响区评价

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00197

Qing Liu, Zijin Feng, Xike Xie, Jianliang Xu, Xin Lin, Christian S. Jensen

引用次数: 2

TableView: A Visual Interface for Generating Preview Tables of Entity Graphs 生成实体图预览表的可视化界面

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00190

Sona Hasani, Ning Yan, Chengkai Li

引用次数: 3

Impatience Is a Virtue: Revisiting Disorder in High-Performance Log Analytics 急躁是一种美德:重新审视高性能日志分析中的紊乱

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00067

B. Chandramouli, J. Goldstein, Yinan Li

引用次数: 6

Keyword Search on Temporal Graphs 时间图的关键字搜索

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00261

Ziyang Liu, Chong Wang, Yi Chen

引用次数: 2

A Novel Framework for Constructing Partially Monotone Rule Ensembles 一种构造部分单调规则集合的新框架

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00140

Christopher Bartley, Wei Liu, Mark Reynolds

引用次数: 3

Aurum: A Data Discovery System Aurum:数据发现系统

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00094

R. Fernandez, Ziawasch Abedjan, F. Koko, Gina Yuan, S. Madden, M. Stonebraker

{"title":"Aurum: A Data Discovery System","authors":"R. Fernandez, Ziawasch Abedjan, F. Koko, Gina Yuan, S. Madden, M. Stonebraker","doi":"10.1109/ICDE.2018.00094","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00094","url":null,"abstract":"Organizations face a data discovery problem when their analysts spend more time looking for relevant data than analyzing it. This problem has become commonplace in modern organizations as: i) data is stored across multiple storage systems, from databases to data lakes, to the cloud; ii) data scientists do not operate within the limits of well-defined schemas or a small number of data sources—instead, to answer complex questions they must access data spread across thousands of data sources. To address this problem, we capture relationships between datasets in an enterprise knowledge graph (EKG), which helps users to navigate among disparate sources. The contribution of this paper is AURUM, a system to build, maintain and query the EKG. To build the EKG, we introduce a Two-step process which scales to large datasets and requires only one-pass over the data, avoiding overloading the source systems. To maintain the EKG without re-reading all data every time, we introduce a resource-efficient sampling signature (RESS) method which works by only using a small sample of the data. Finally, to query the EKG, we introduce a collection of composable primitives, thus allowing users to define many different types of discovery queries. We describe our experience using AURUM in three corporate scenarios and do a performance evaluation of each component.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116385756","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: 151