2021年最新SCI期刊影响因子查询系统
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 期刊详细信息
基本信息
期刊名称 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
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期刊ISSN | 1532-0626 |
期刊官方网站 | http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-0634 |
是否OA | 否 |
出版商 | John Wiley and Sons Ltd |
出版周期 | Semimonthly |
始发年份 | 2001 |
年文章数 | 244 |
最新影响因子 | 1.831(2021) |
中科院SCI期刊分区
大类学科 | 小类学科 | Top | 综述 |
---|---|---|---|
工程技术4区 | COMPUTER SCIENCE, SOFTWARE ENGINEERING 计算机:软件工程4区 | 否 | 否 |
COMPUTER SCIENCE, THEORY & METHODS 计算机:理论方法4区 |
CiteScore
CiteScore排名 | CiteScore | SJR | SNIP | ||
---|---|---|---|---|---|
学科 | 排名 | 百分位 | 1.59 | 0.313 | 0.851 |
Computer Science Computational Theory and Mathematics |
43 / 113 | 62% |
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Mathematics Theoretical Computer Science |
48 / 118 | 59% |
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Computer Science Computer Networks and Communications |
128 / 274 | 52% |
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Computer Science Software |
205 / 360 | 43% |
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Computer Science Computer Science Applications |
278 / 569 | 51% |
补充信息
自引率 | 6.30% |
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H-index | 52 |
SCI收录状况 |
Science Citation Index Expanded |
官方审稿时间 | |
网友分享审稿时间 | 数据统计中,敬请期待。 |
PubMed Central (PML) | http://www.ncbi.nlm.nih.gov/nlmcatalog?term=1532-0626%5BISSN%5D |
投稿指南
期刊投稿网址 | http://mc.manuscriptcentral.com/cpe |
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收稿范围 | Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing. Emphasis on novel research related to practice and experience in these areas should be an essential aspect of contributions, rather than addressing theoretical aspects. Submissions should involve or imply significant concurrency and/or computational issues. Within these broad areas, the scope of CCPE includes the design, implementation, and optimization of compute and data-intensive applications for parallel and distributed systems. This includes the development of novel concurrent algorithms and applications, their parallel performance analysis and modelling, and new programming or modelling languages and relevant methodologies for composing them. Areas relevant to compute and data-intensive applications include, but are not limited to, large-scale computational science, artificial intelligence, and the processing of voluminous datasets from satellites, scientific experiments, sensor networks, medical instruments, and other sources. Techniques for resource management in the context of parallel and distributed systems, and energy-aware computing are also topics of interest. |
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