Big Data

Journal Title：Big Data

Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions.

Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government.

Big Data coverage includes:

Big data industry standards,

New technologies being developed specifically for big data,

Data acquisition, cleaning, distribution, and best practices,

Data protection, privacy, and policy,

Business interests from research to product,

The changing role of business intelligence,

Visualization and design principles of big data infrastructures,

Physical interfaces and robotics,

Social networking advantages for Facebook, Twitter, Amazon, Google, etc,

Opportunities around big data and how companies can harness it to their advantage.

《大数据》是领先的同行评审期刊，涵盖收集、分析和传播大量数据的挑战和机遇。该期刊解答了围绕这一强大且不断发展的数据科学领域的问题，并促进了研究人员、企业经理、分析师、开发人员、数据科学家、物理学家、统计学家、基础设施开发人员、学者和政策制定者的努力，以改善其企业和机构内的运营、盈利能力和沟通。

该期刊涵盖了广泛的学科，重点关注新颖的大数据技术、政策和创新，它汇集了社区以应对当前的挑战，并采取有效的措施来组织、存储、传播、保护、操纵，最重要的是，找到最有效的策略，使这些海量的信息为社会、行业、学术界和政府带来利益。

大数据报道包括：

大数据行业标准，

专门为大数据开发的新技术，

数据获取、清理、分发和最佳实践，

数据保护、隐私和政策，

从研究到产品，

商业智能的角色变化，

大数据基础设施的可视化和设计原则，

物理接口和机器人技术，

Facebook、Twitter、Amazon、Google 等的社交网络优势，

大数据带来的机遇以及公司如何利用大数据为自己谋利。

Big Data创刊于2013年，由Mary Ann Liebert Inc.出版商出版，收稿方向涵盖COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS - COMPUTER SCIENCE, THEORY & METHODS全领域，此期刊水平偏中等偏靠后，在所属细分领域中专业影响力一般，过审相对较易，如果您文章质量佳，选择此期刊，发表机率较高。平均审稿速度 ，影响因子指数2.6，该期刊近期没有被列入国际期刊预警名单，广大学者值得一试。