Data Science and Engineering最新文献_第6页

Translating a Distributed Relational Database to a Document Database 将分布式关系数据库转换为文档数据库

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-03-03 DOI: 10.1007/s41019-022-00181-9

Muon Ha, Y. Shichkina

引用次数: 4

Disentangled Graph Recurrent Network for Document Ranking 用于文档排序的解纠缠图递归网络

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-02-15 DOI: 10.1007/s41019-022-00179-3

Qian Dong, Shuzi Niu, Tao Yuan, Yucheng Li

引用次数: 9

Critical Correlation of Predictors for an Efficient Risk Prediction Framework of ICU Patient Using Correlation and Transformation of MIMIC-III Dataset 利用MIMIC-III数据集的关联和转换，建立有效的ICU患者风险预测框架的关键相关预测因子

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-02-08 DOI: 10.1007/s41019-022-00176-6

Sarika R. Khope, Susan Elias

引用次数: 4

Editorial: Updates to the DSE Leadership and Editorial Board 社论:DSE领导层和编辑委员会的最新情况

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-02-03 DOI: 10.1007/s41019-022-00175-7

T. Sellis

引用次数: 1

Top k Optimal Sequenced Route Query with POI Preferences 基于POI偏好的Top k最优顺序路由查询

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-02-03 DOI: 10.1007/s41019-022-00177-5

Huaijie Zhu, Wenbin Li, Wei Liu, Jian Yin, Jianliang Xu

引用次数: 6

Spatio-Temporal Representation Learning with Social Tie for Personalized POI Recommendation 基于社会关联的时空表征学习用于个性化POI推荐

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-01-31 DOI: 10.1007/s41019-022-00180-w

Shaojie Dai, Yanwei Yu, H. Fan, Junyu Dong

引用次数: 25

Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling 开放世界中的实体对齐:一种无监督的置信度建模方法

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-01-29 DOI: 10.1007/s41019-022-00178-4

Xiang Zhao, Weixin Zeng, Jiuyang Tang, Xinyi Li, Minnan Luo, Qinghua Zheng

引用次数: 3

Link Prediction on Complex Networks: An Experimental Survey. 复杂网络的链路预测:实验研究。

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-01-01 Epub Date: 2022-06-21 DOI: 10.1007/s41019-022-00188-2

Haixia Wu, Chunyao Song, Yao Ge, Tingjian Ge

{"title":"Link Prediction on Complex Networks: An Experimental Survey.","authors":"Haixia Wu, Chunyao Song, Yao Ge, Tingjian Ge","doi":"10.1007/s41019-022-00188-2","DOIUrl":"https://doi.org/10.1007/s41019-022-00188-2","url":null,"abstract":"Complex networks have been used widely to model a large number of relationships. The outbreak of COVID-19 has had a huge impact on various complex networks in the real world, for example global trade networks, air transport networks, and even social networks, known as racial equality issues caused by the spread of the epidemic. Link prediction plays an important role in complex network analysis in that it can find missing links or predict the links which will arise in the future in the network by analyzing the existing network structures. Therefore, it is extremely important to study the link prediction problem on complex networks. There are a variety of techniques for link prediction based on the topology of the network and the properties of entities. In this work, a new taxonomy is proposed to divide the link prediction methods into five categories and a comprehensive overview of these methods is provided. The network embedding-based methods, especially graph neural network-based methods, which have attracted increasing attention in recent years, have been creatively investigated as well. Moreover, we analyze thirty-six datasets and divide them into seven types of networks according to their topological features shown in real networks and perform comprehensive experiments on these networks. We further analyze the results of experiments in detail, aiming to discover the most suitable approach for each kind of network.","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"7 3","pages":"253-278"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9211798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40398440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Visual Data Analysis with Task-Based Recommendations. 基于任务的建议的可视化数据分析。

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-01-01 Epub Date: 2022-09-13 DOI: 10.1007/s41019-022-00195-3

Leixian Shen, Enya Shen, Zhiwei Tai, Yihao Xu, Jiaxiang Dong, Jianmin Wang

引用次数: 5

Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods. 代理模型的降维:综合方法综述。

IF 4.2 2区计算机科学

Data Science and Engineering Pub Date : 2022-01-01 Epub Date: 2022-08-21 DOI: 10.1007/s41019-022-00193-5

Chun Kit Jeffery Hou, Kamran Behdinan

{"title":"Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods.","authors":"Chun Kit Jeffery Hou, Kamran Behdinan","doi":"10.1007/s41019-022-00193-5","DOIUrl":"https://doi.org/10.1007/s41019-022-00193-5","url":null,"abstract":"Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering. The modeling demand exponentially increases with complexity and number of system parameters, which consequently requires higher-dimensional engineering solving techniques. This is known as the curse of dimensionality. Surrogate models are commonly used to replace costly computational simulations and modeling of complex geometries. However, an ongoing challenge is to reduce execution and memory consumption of high-complexity processes, which often exhibit nonlinear phenomena. Dimensionality reduction algorithms have been employed for feature extraction, selection, and elimination for simplifying surrogate models of high-dimensional problems. By applying dimensionality reduction to surrogate models, less computation is required to generate surrogate model parts while retaining sufficient representation accuracy of the full process. This paper aims to review the current literature on dimensionality reduction integrated with surrogate modeling methods. A review of the current state-of-the-art dimensionality reduction and surrogate modeling methods is introduced with a discussion of their mathematical implications, applications, and limitations. Finally, current studies that combine the two topics are discussed and avenues of further research are presented.","PeriodicalId":52220,"journal":{"name":"Data Science and Engineering","volume":"7 4","pages":"402-427"},"PeriodicalIF":4.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40672089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12