Information Systems

雜志介紹

Information Systems雜志介紹

《Information Systems》是一本以Multi-Language為主的未開放獲取國際優秀期刊，中文名稱信息系統，本刊主要出版、報道計算機科學-COMPUTER SCIENCE, INFORMATION SYSTEMS領域的研究動態以及在該領域取得的各方面的經驗和科研成果，介紹該領域有關本專業的最新進展，探討行業發展的思路和方法，以促進學術信息交流，提高行業發展。該刊已被國際權威數據庫SCIE收錄，為該領域相關學科的發展起到了良好的推動作用，也得到了本專業人員的廣泛認可。該刊最新影響因子為3，最新CiteScore 指數為9.4。

本刊近期中國學者發表的論文主要有：

• A detection method for hybrid attacks in recommender systems

  Author: Hao, Yaojun; Meng, Guoyan; Wang, Jian; Zong, Chunmei

• HCDC: A novel hierarchical clustering algorithm based on density-distance cores for data sets with varying density

  Author: Yang, Qi-Fen; Gao, Wan-Yi; Han, Gang; Li, Zi-Yang; Tian, Meng; Zhu, Shu-Hua; Deng, Yu-hui

• Guided conditional functional dependency discovery

  Author: Jiang, Sijia; Tan, Zijing; Wang, Jiawei; Wang, Zhikang; Ma, Shuai

• Virtual self-adaptive bitmap for online cardinality estimation

  Author: Lu, Jie; Chen, Hongchang; Zhang, Jianpeng; Hu, Tao; Sun, Penghao; Zhang, Zhen

英文介紹

Information Systems雜志英文介紹

Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.

Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.