Data Science and Engineering
Data Science and Engineering (DSE) is an international, peer-reviewed, and open access journal published under the brand SpringerOpen, on behalf of the China Computer Federation (CCF). 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.
The journal publishes high-quality, original research papers, brief reports, and critical reviews in all theoretical, technological, and interdisciplinary studies that make up the fields of data science and engineering and its applications.
The publication costs are covered by The China Computer Federation (CCF) and Nanjing Sinovatio Technology Co. Ltd so authors do not need to pay an article-processing charge.
DSE operates a double-blind peer-review system, where the reviewers do not know the names or affiliations of the authors and the reviewer reports provided to the authors are anonymous.
All articles published are made freely and permanently accessible online immediately upon publication, without subscription charges or registration barriers. Further information about open access can be found at https://www.springeropen.com/about/open-access.
As authors of articles published in DSE you are the copyright holders of your article and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate your article, according to the SpringerOpen copyright and license agreement (https://www.springeropen.com/get-published/copyright/copyright-and-license-agreement).
For those of you who are US government employees or are prevented from being copyright holders for similar reasons, SpringerOpen can accommodate non-standard copyright lines. Please contact email@example.com if further information is needed.
Using GUHA Data Mining Method in Analyzing Road Traffic Accidents Occurred in the Years 2004–2008 in Finland
Esko Turunen (August 2017)
- Journal Title
- Data Science and Engineering
- Volume 1 / 2016 - Volume 2 / 2017
- Print ISSN
- Online ISSN
- Springer Berlin Heidelberg
- Additional Links
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