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. DSE welcomes papers that explore the above subjects. Specific topics include, but are not limited to: (a) the nature and quality of data, (b) the computational complexity of data-intensive computing,(c) new methods for the design and analysis of the algorithms for solving problems with big data input,(d) collection and integration of data collected from internet and sensing devises or sensor networks, (e) representation, modeling, and visualization of  big data,(f)  storage, transmission, and management of big data,(g) methods and algorithms of  data intensive computing, such asmining big data,online analysis processing of big data,big data-based machine learning, big data based decision-making, statistical computation of big data, graph-theoretic computation of big data, linear algebraic computation of big data, and  big data-based optimization. (h) hardware systems and software systems for data-intensive computing, (i) data security, privacy, and trust, and(j) novel applications of big data.

《数据科学与工程》（DSE）杂志响应了信息技术发展重点从 CPU 密集型计算到数据密集型计算的显著变化，其中数据（尤其是大数据）的有效应用变得至关重要。新兴学科数据科学与工程是一门跨学科领域，整合了计算机科学、统计学、信息科学和其他领域的理论和方法，专注于数据收集和管理、数据集成和关联、从海量数据集中提取信息和知识以及在不同应用领域使用数据的高效技术和系统的基础和工程。DSE 专注于理论背景和先进的工程方法，旨在为研究人员、专业人士和行业从业者提供一个主要论坛，分享他们在这个快速增长领域的知识。

它深入报道了数据科学和数据工程密切相关领域的最新进展。更具体地说，DSE 涵盖四个领域：（i）数据本身，即数据（尤其是大数据）的性质和质量；（ii）从数据（尤其是大数据）中提取信息的原理； (iii) 数据密集型计算背后的理论；(iv) 用于分析和管理大数据的技术和系统。DSE 欢迎探讨上述主题的论文。具体主题包括但不限于：(a) 数据的性质和质量；(b) 数据密集型计算的计算复杂性；(c) 用于解决大数据输入问题的算法的设计和分析的新方法；(d) 从互联网和传感设备或传感器网络收集的数据的收集和集成；(e) 大数据的表示、建模和可视化；(f) 大数据的存储、传输和管理；(g) 数据密集型计算的方法和算法，如大数据挖掘、大数据在线分析处理、基于大数据的机器学习、基于大数据的决策、大数据统计计算、大数据图论计算、大数据线性代数计算以及基于大数据的优化。 (h) 数据密集型计算的硬件系统和软件系统，(i) 数据安全、隐私和信任，以及(j) 大数据的新应用。

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