Data Science and Engineering

Aims & Scope

The journal of Data Science and Engineering (DSE) responds to the remarkable change in the focus of information technology development from CPU-intensive computation to data-intensive computation, where the effective application of data, especially big data, becomes vital.

The emerging discipline data science and engineering, an interdisciplinary field integrating theories and methods from computer science, statistics, information science, and other fields, focuses on the foundations and engineering of efficient and effective techniques and systems for data collection and management, for data integration and correlation, for information and knowledge extraction from massive data sets, and for data use in different application domains.

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.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Engineering, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Computer Science and Engineering, ensuring accessibility and quality in research dissemination.

This journal does not charge a mandatory Article Processing Charge (APC). However, optional open access publication may incur fees based on the publisher’s policies.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

  Citation: 274

  Authors: Haitao, Guoliang

• Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

  Citation: 165

  Authors: Siuly, Yanchun

• Big Data Reduction Methods: A Survey

  Citation: 147

  Authors: Muhammad Habib, Chee Sun, Assad, Prem Prakash, Teh Ying, Samee U.

• Approximate Query Processing: What is New and Where to Go?

  Citation: 97

  Authors: Kaiyu, Guoliang

• Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

  Citation: 97

  Authors: Rubina, Khandakar, Hua, Yanchun, Jiangang, Kate

• Representation Learning in Multi-view Clustering: A Literature Review

  Citation: 92

  Authors: Man-Sheng, Jia-Qi, Xiang-Long, Bao-Yu, Chang-Dong, Dong, Jian-Huang

• Deep Learning for User Interest and Response Prediction in Online Display Advertising

  Citation: 87

  Authors: Zhabiz, Xingquan, Arthur, Michael

• Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization

  Citation: 83

  Authors: Shan, Songsong, Liwei, Zhiyong

• Big Data Privacy: Challenges to Privacy Principles and Models

  Citation: 80

  Authors: Jordi, Josep

• A Practical Privacy-Preserving Recommender System

  Citation: 79

  Authors: Shahriar, Xun, Ibrahim