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Data Science And Engineering

Data Science And Engineering

SCIE

国际简称:Data Science And Engineering  参考译名:数据科学与工程

  • 中科院分区

    2区

  • CiteScore分区

    Q1

  • JCR分区

    Q1

基本信息:
ISSN:2364-1185
E-ISSN:2364-1541
是否OA:未开放
是否预警:否
TOP期刊:是
出版信息:
出版地区:Germany
出版商:Springer Nature
出版语言:English
出版周期:4 issues per year
研究方向:Engineering-Computational Mechanics
评价信息:
影响因子:4.1
CiteScore指数:9.6
SJR指数:1.212
SNIP指数:1.816
发文数据:
Gold OA文章占比:100.00%
研究类文章占比:87.50%
年发文量:48
自引率:0.0238...
开源占比:0.988
出版撤稿占比:
出版国人文章占比:0
OA被引用占比:
英文简介 期刊介绍 CiteScore数据 中科院SCI分区 JCR分区 发文数据 常见问题

英文简介Data Science And Engineering期刊介绍

The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital. The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains. Focusing on the theoretical background and advanced engineering approaches, DSE aims to offer a prime forum for researchers, professionals, and industrial practitioners to share their knowledge in this rapidly growing area.

It provides in-depth coverage of the latest advances in the closely related fields of data science and data engineering. More specifically, DSE covers four areas: (i) the data itself, i.e., the nature and quality of the data, especially big data; (ii) the principles of information extraction from data, especially big data; (iii) the theory behind data-intensive computing; and (iv) the techniques and systems used to analyze and manage big data. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.

期刊简介Data Science And Engineering期刊介绍

《Data Science And Engineering》是一本计算机科学优秀杂志。致力于发表原创科学研究结果,并为计算机科学各个领域的原创研究提供一个展示平台,以促进计算机科学领域的的进步。该刊鼓励先进的、清晰的阐述,从广泛的视角提供当前感兴趣的研究主题的新见解,或审查多年来某个重要领域的所有重要发展。该期刊特色在于及时报道计算机科学领域的最新进展和新发现新突破等。该刊近一年未被列入预警期刊名单,目前已被权威数据库SCIE收录,得到了广泛的认可。

该期刊投稿重要关注点:

Cite Score数据(2026年6月最新版)Data Science And Engineering Cite Score数据

  • CiteScore:9.6
  • SJR:1.212
  • SNIP:1.816
学科类别 分区 排名 百分位
大类:Computer Science 小类:Software Q1 77 / 503

84%

大类:Computer Science 小类:Computer Science Applications Q1 156 / 1022

84%

大类:Computer Science 小类:Information Systems Q1 83 / 519

84%

大类:Computer Science 小类:Artificial Intelligence Q1 102 / 570

82%

CiteScore 是由Elsevier(爱思唯尔)推出的另一种评价期刊影响力的文献计量指标。反映出一家期刊近期发表论文的年篇均引用次数。CiteScore以Scopus数据库中收集的引文为基础,针对的是前四年发表的论文的引文。CiteScore的意义在于,它可以为学术界提供一种新的、更全面、更客观地评价期刊影响力的方法,而不仅仅是通过影响因子(IF)这一单一指标来评价。

历年Cite Score趋势图

中科院SCI分区Data Science And Engineering 中科院分区

《新锐期刊分区表》(2026年3月发布) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 2区 COMPUTER SCIENCE, INFORMATION SYSTEMS 计算机:信息系统 COMPUTER SCIENCE, THEORY & METHODS 计算机:理论方法 2区 1区
期刊分区表(2025年3月升级版) 综述期刊:否 Top期刊:是
大类学科 分区 小类学科 分区
计算机科学 1区 COMPUTER SCIENCE, INFORMATION SYSTEMS 计算机:信息系统 COMPUTER SCIENCE, THEORY & METHODS 计算机:理论方法 1区 1区
期刊分区表(2023年12月升级版) 综述期刊:否 Top期刊:否
大类学科 分区 小类学科 分区
计算机科学 2区 COMPUTER SCIENCE, INFORMATION SYSTEMS 计算机:信息系统 COMPUTER SCIENCE, THEORY & METHODS 计算机:理论方法 2区 2区

中科院分区表 是以客观数据为基础,运用科学计量学方法对国际、国内学术期刊依据影响力进行等级划分的期刊评价标准。它为我国科研、教育机构的管理人员、科研工作者提供了一份评价国际学术期刊影响力的参考数据,得到了全国各地高校、科研机构的广泛认可。

中科院分区表 将所有期刊按照一定指标划分为1区、2区、3区、4区四个层次,类似于“优、良、及格”等。最开始,这个分区只是为了方便图书管理及图书情报领域的研究和期刊评估。之后中科院分区逐步发展成为了一种评价学术期刊质量的重要工具。

历年中科院分区趋势图

JCR分区Data Science And Engineering JCR分区

2025-2026年最新版
按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, INFORMATION SYSTEMS ESCI Q2 103 / 266

61.5

学科:COMPUTER SCIENCE, THEORY & METHODS ESCI Q1 35 / 146

76.4

学科:COMPUTER SCIENCE, INFORMATION SYSTEMS ESCI Q2 89 / 266

66.73

学科:COMPUTER SCIENCE, THEORY & METHODS ESCI Q1 33 / 147

77.89

2023-2024年最新版
按JCI指标学科分区 收录子集 分区 排名 百分位
学科:COMPUTER SCIENCE, INFORMATION SYSTEMS ESCI Q1 43 / 249

82.9

学科:COMPUTER SCIENCE, THEORY & METHODS ESCI Q1 19 / 143

87.1

学科:COMPUTER SCIENCE, INFORMATION SYSTEMS ESCI Q2 72 / 251

71.51

学科:COMPUTER SCIENCE, THEORY & METHODS ESCI Q1 24 / 143

83.57

JCR分区的优势在于它可以帮助读者对学术文献质量进行评估。不同学科的文章引用量可能存在较大的差异,此时单独依靠影响因子(IF)评价期刊的质量可能是存在一定问题的。因此,JCR将期刊按照学科门类和影响因子分为不同的分区,这样读者可以根据自己的研究领域和需求选择合适的期刊。

历年影响因子趋势图

本刊中国学者近年发表论文

  • 1、Efficient Label-Constrained Path Queries on Time-Dependent Graphs via LP-Tree Decompositio

    Author: Wang, Yishu; Chu, Jinlong; Yuan, Ye; Sun, Yongjiao; Zhao, Xiangguo; Ji, Hangxu

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-026-00345-x

  • 2、A Survey on Modern Deep Learning Techniques for Traffic Volume Predictio

    Author: Qin, Zixin; Wang, Mengxiang; Zhu, Huaijie; Lee, Wang-Chien; Cui, Ningning; Yu, Jianxing; Tian, Zeming; Hong, Yixin; Yin, Jian

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-025-00340-8

  • 3、Unsupervised Anomaly Detection on Attributed Multiplex Heterogeneous Network

    Author: Cao, Yuehang; Wang, Kai; Wei, Xuan; Zhao, Xiang

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-025-00339-1

  • 4、Enumerating Cliques on k-Partite Graph

    Author: Li, Faming; Qiu, Shengli; Ning, Baoling; Yang, Xiaochun; Wang, Bin; Li, Jianzhong

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-025-00338-2

  • 5、A Survey of Generative Techniques for Spatial-Temporal Data Minin

    Author: Zhang, Qianru; Wang, Haixin; Wen, Honggang; Long, Cheng; Su, Liangcai; He, Xingwei; Wu, Tailin; Jensen, Christian S.; Yiu, Siu-Ming; Yin, Hongzhi

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-026-00346-w

  • 6、IBformer: Inductive Bias is Necessary for Multivariate Time Series Forecastin

    Author: Shi, Haoyuan; Meng, Xiangfu; Zhang, Yongqin; Lu, Jianxu; Wang, Jia

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-025-00337-3

  • 7、Sparse Gradient Training for Recommender System

    Author: Qu, Yunke; Qu, Liang; Chen, Tong; Zhao, Xiangyu; Li, Jianxin; Yin, Hongzhi

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-025-00327-5

  • 8、Generating Counterfactual Temporal Motifs: Unraveling the Mysteries of Temporal Graph Neural Network

    Author: Zhao, Yibowen; Xu, Yonghui; Liu, Ning; Cui, Lizhen; Li, Qingzhong

    Journal: DATA SCIENCE AND ENGINEERING. 2026; Vol. , Issue , pp. -. DOI: 10.1007/s41019-025-00321-x

投稿常见问题