Frontiers In Big Data

Frontiers In Big Data

大数据前沿

  • 3区 中科院分区
  • Q1 JCR分区

期刊简介

《Frontiers In Big Data》是由Frontiers Media S.A.出版社创办的英文国际期刊(ISSN: 2624-909X),该期刊长期致力于计算机:信息系统领域的创新研究,主要研究方向为Multiple。作为SCIE收录期刊(JCR分区 Q1,中科院 3区),本刊采用OA未开放获取模式(OA占比%),以发表计算机:信息系统领域等方向的原创性研究为核心(研究类文章占比85.87%%)。凭借严格的同行评审与高效编辑流程,期刊年载文量精选控制在92篇,确保学术质量与前沿性。成果覆盖Web of Science、Scopus等国际权威数据库,为学者提供推动计算机科学领域高水平交流平台。

投稿咨询

投稿提示

Frontiers In Big Data审稿周期约为13 Weeks。该刊近年未被列入国际预警名单,年发文量约92篇,录用竞争适中,主题需确保紧密契合计算机科学前沿。投稿策略提示:避开学术会议旺季投稿以缩短周期,语言建议专业润色提升可读性。

  • 计算机科学 大类学科
  • English 出版语言
  • 是否预警
  • SCIE 期刊收录
  • 92 发文量

中科院分区

《新锐期刊分区表》(2026年3月发布)

Top期刊 综述期刊 大类学科 小类学科
计算机科学
3区
COMPUTER SCIENCE, INFORMATION SYSTEMS 计算机:信息系统 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机:跨学科应用 MULTIDISCIPLINARY SCIENCES 综合性期刊
4区 4区 3区

期刊分区表(2025年3月升级版)

Top期刊 综述期刊 大类学科 小类学科
计算机科学
4区
COMPUTER SCIENCE, INFORMATION SYSTEMS 计算机:信息系统 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 计算机:跨学科应用 MULTIDISCIPLINARY SCIENCES 综合性期刊
4区 4区 4区

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 110 / 266

58.8

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ESCI Q2 85 / 185

54.3

学科:MULTIDISCIPLINARY SCIENCES ESCI Q1 30 / 140

78.9

学科:COMPUTER SCIENCE, INFORMATION SYSTEMS ESCI Q3 157 / 266

41.17

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ESCI Q3 119 / 185

35.95

学科:MULTIDISCIPLINARY SCIENCES ESCI Q2 61 / 140

56.79

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 Q3 126 / 249

49.6

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ESCI Q3 91 / 169

46.4

学科:MULTIDISCIPLINARY SCIENCES ESCI Q2 45 / 134

66.8

学科:COMPUTER SCIENCE, INFORMATION SYSTEMS ESCI Q3 151 / 251

40.04

学科:COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ESCI Q3 105 / 169

38.17

学科:MULTIDISCIPLINARY SCIENCES ESCI Q2 56 / 135

58.89

CiteScore(2026年6月最新版)

CiteScore SJR SNIP CiteScore 排名
CiteScore:7.3 SJR:0.691 SNIP:1.369
学科类别 分区 排名 百分位
大类:Computer Science 小类:Computer Science (miscellaneous) Q1 39 / 179

78%

大类:Computer Science 小类:Information Systems Q1 123 / 519

76%

大类:Computer Science 小类:Artificial Intelligence Q2 144 / 570

74%

期刊发文

  • A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterio

    Author: Zhong, Kunhua; Chen, Han; Sun, Qilong; Wang, Peng; Liu, Zhenbei; Chen, Yuwen

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1811110

  • A disease potential-driven graph attention model for comorbidity risk prediction of hypertensio

    Author: Zhou, Leming; Qin, Hanshu; Yang, Yanmei; Huang, Gang; Liu, Zhigang

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1814157

  • Jingdezhen ceramic culture in the digital era: a qualitative inquiry into digital dissemination and platform innovatio

    Author: Huang, Qiuyang; Chen, Zhengjun

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1752142

  • GFTrans: an on-the-fly static analysis framework for code performance profilin

    Author: Li, Jie; Wen, Yunbao; Liu, Jingxin; Zeng, Biqing; Mirjalili, Seyedali

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1779935

  • Federated learning for teacher data privacy protection: a study in the context of the PIP

    Author: Chen, Shanwei; Qi, Xiu Zhi; Han, Xue Hui; Fan, Zhao Chen; Wang, Le Le

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1681382

  • Dynamic transfer learning with co-occurrence-guided multi-source fusion for urban spatio-temporal crime predictio

    Author: Cui, Chen; Zheng, Ziwan; Du, Hao; Wang, Wen

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1697392

  • Depression detection through dual-stream modeling with large language models: a fusion-based transfer learning framework integrating BERT and T5 representation

    Author: Wang, Na; Zhang, Weijia; Kamil, Raja; Renner, Ian; Al-Haddad, Syed Abdul Rahman; Ibrahim, Normala; Zhao, Zhen

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 8, Issue , pp. -. DOI: 10.3389/fdata.2025.1651290

  • Adaptive core-enhanced latent factor model for highly accurate QoS predictio

    Author: Ai, Siqi; Li, Peixin; Fang, Hao; Xia, Yonghui

    Journal: FRONTIERS IN BIG DATA. 2026; Vol. 9, Issue , pp. -. DOI: 10.3389/fdata.2026.1775728