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ACM《数据科学会刊》征文

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ACM 近日宣布创建期刊“数据科学会刊”(ACM Transactions on Data Science),主编为新加坡国立大学黄铭钧(Beng Chin Ooi教授(曾获得2016年CCF海外杰出贡献奖)。CCF 大数据专委主任、北京理工大学副校长梅宏院士等4人为资深编辑。期刊网站:https://tds.acm.org/,欢迎投稿。

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Editor-in-Chief

Beng Chin Ooi, National University of Singapore

Senior Associate Editors

Mike Franklin, University of Chicago

H.V. Jagadish, University of Michigan

Hong Mei, Beijing Institute of Technology and Peking University

Renee Miller, University of Toronto

Data Science relies on the massive volumes of diverse data generated from all forms of human activity and interaction with the environment to make decisions and to solve problems. Traditional methods for managing and processing data have been scaled to address its growth, but new approaches are required to deal with these heterogeneous, high velocity, very large data sources of varying quality, coverage, and semantics. There are challenges at every stage. Addressing these challenges requires innovations in a wide range of computing sub-disciplines, from computer architecture to human computer interaction,and from data analytics to recommendations. ACM Transactions on Data Science (TDS) will serve as the

premier forum for describing and advancing the state of the art on this important topic.

Scope

The scope of the TDS includes cross-disciplinary innovative research ideas, algorithms, systems, theory and applications for data science. Papers that address challenges at every stage, from acquisition on, through data cleaning, transformation, representation, integration, indexing, modeling, analysis, visualization, and interpretation while retaining privacy, fairness, provenance, transparency, and provision of social benefit, within the context of big data, fall within the scope of the journal. The objective of the journal is to provide a forum for cross-cutting research results that contribute to data science. Papers that address core technologies without clear evidence that they propose multi/cross-disciplinary technologies and approaches designed for management and processing of large volumes of data, and for data-driven decision making will be out of scope of this journal.