2018 IEEE 34th International Conference on Data Engineering (ICDE)最新文献_第6页

Elton: A Cloud Resource Scaling-Out Manager for NoSQL Databases Elton: NoSQL数据库的云资源扩展管理器

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00196

A. Naskos, A. Gounaris, I. Konstantinou

引用次数: 5

Inf2vec: Latent Representation Model for Social Influence Embedding 社会影响嵌入的潜在表征模型

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00089

Shanshan Feng, G. Cong, Arijit Khan, Xiucheng Li, Yong Liu, Y. M. Chee

引用次数: 51

Accelerating Concurrent Workloads with CPU Cache Partitioning 通过CPU Cache分区加速并发工作负载

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00047

Stefan Noll, J. Teubner, Norman May, Alexander Böhm

引用次数: 14

Rainbow: Adaptive Layout Optimization for Wide Tables Rainbow:宽表格的自适应布局优化

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00200

Haoqiong Bian, Youxian Tao, Guodong Jin, Yueguo Chen, Xiongpai Qin, Xiaoyong Du

引用次数: 5

Parallel Reasoning of Graph Functional Dependencies 图函数依赖的并行推理

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00060

W. Fan, Xueli Liu, Yingjie Cao

引用次数: 6

Actor-Oriented Database Systems 面向参与者的数据库系统

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00010

P. Bernstein

引用次数: 9

Efficiently Reinforcing Social Networks over User Engagement and Tie Strength 有效地加强用户参与和联系强度的社会网络

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00057

Fan Zhang, Ying Zhang, Lu Qin, W. Zhang, Xuemin Lin

引用次数: 26

ULISSE: ULtra Compact Index for Variable-Length Similarity Search in Data Series 数据序列中变长相似搜索的超紧凑索引

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00149

Michele Linardi, Themis Palpanas

引用次数: 27

Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances 广义动态时间翘曲:释放隐藏在逐点距离中的翘曲能力

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00054

R. Neamtu, Ramoza Ahsan, Elke A. Rundensteiner, G. N. Sárközy, Eamonn J. Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering

{"title":"Generalized Dynamic Time Warping: Unleashing the Warping Power Hidden in Point-Wise Distances","authors":"R. Neamtu, Ramoza Ahsan, Elke A. Rundensteiner, G. N. Sárközy, Eamonn J. Keogh, Hoang Anh Dau, Cuong Nguyen, Charles Lovering","doi":"10.1109/ICDE.2018.00054","DOIUrl":"https://doi.org/10.1109/ICDE.2018.00054","url":null,"abstract":"Domain-specific distances preferred by analysts for exploring similarities among time series tend to be \"point-to-point\" distances. Unfortunately, this point-wise nature limits their ability to perform meaningful comparisons between sequences of different lengths and with temporal mis-alignments. Analysts instead need \"elastic\" alignment tools such as Dynamic Time Warping (DTW) to perform such flexible comparisons. However, the existing alignment tools are limited in that they do not incorporate diverse distances. To address this shortcoming, our work introduces the first conceptual framework called Generalized Dynamic Time Warping (GDTW) that supports now alignment (warping) of a large array of domain-specific distances in a uniform manner. While the classic DTW and its prior extensions focus on the Euclidean Distance, our GDTW is the first method that generalizes the ubiquitous DTW and \"extends\" its warping capabilities to a rich diversity of point-to-point distances. Based on our GDTW paradigm that preserves the efficiency of the dynamic programming paradigm of DTW, we design an abstraction that implemented by our GDTW Design Tool enables analysts to \"warp\" new distances with little programming effort. Through extensive evaluation studies on 85 real public domain benchmark datasets, we show that our newly warped distances offer higher classification accuracy than the previously available distances for the majority of these datasets. Further, our case study on heart arrhythmia data illustrates the utility of the new distances enabled by our GDTW warping methodology.","PeriodicalId":247940,"journal":{"name":"2018 IEEE 34th International Conference on Data Engineering (ICDE)","volume":"13 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":"114805721","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: 18

The Data Exploration Game 数据探索游戏

2018 IEEE 34th International Conference on Data Engineering (ICDE) Pub Date : 2018-04-16 DOI: 10.1109/ICDE.2018.00206

Ben McCamish, Arash Termehchy, B. Touri

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引用次数: 0