Big Data Research 1 (2014) 1

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Big Data Research

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The era of big data is approaching as large and complex data sets are being collected for diverse reasons through all kinds of technolo-es or approaches including mobile devices, remote sensing technologies, software logs, wireless sensor networks, social media etc. The g data sets tend to be more unstructured, distributed and complex than ever before. The mainly concerned characteristics of big data n fall into three dimensions: (a) the volume of information that systems must ingest, process and disseminate; (b) the velocity at which formation grows or disappears; (c) the variety in the diversity of data sources and formats. This situation poses signicant challenges on aditional data processing applications and data management tools. The intuitionistic challenges include collecting, storing, transferring, d visualizing all kinds of big data. More importantly, we need effective ways of tuning big data into big insights. The true value of g data lies in the implicit valuable knowledge derived from the analysis of a group of interrelated data sets, which allow deep correla-ns and hidden principles to be found for business trends prediction, health hazard analysis, terrorist threats detection, search engine timization, biological and environmental research, etc. That is where new theories, novel methods and right analytics tools are needed help scientists and business leaders make sense of the volumes of data.Data Science is the study and practice of extracting additional knowledge and deriving valuable insights from data. It calls for multi-

sciplinary approaches that incorporate theories and methods from many elds including mathematics, statistics, pattern recognition, owledge engineering, machine learning, high performance computing, etc. Additionally, Data Science is the science about Data. It ings about a number of new research topics on Data itself such as data life cycle management, data privacy solution, elastic data com-ting, spatial-temporal nature and social aspects of big data. A practitioner of data science is called a data scientist who typically has strong expertise in some scientic discipline, in addition to the ability of working with various elements of mathematics, statistics and mputer science. Data Science is not restricted to only big data. However, the fact that data is scaling up and the invention of tons of w tools for big data analysis open a new era for data science. The rapid development in big data has led to many tools related to big ta storage platform and data analytic platform, which signicantly increase the eciency of study in data science.Big data techniques and data science heavily inuence how we conduct research across various domains including economics, business, ance biological sciences, health care, social sciences and the humanities. From a multi-disciplinary perspective, big data is a newly erging eld that encompasses a number of disciplines. It depends on inter-disciplinary study and practice that can help data science in a competitive edge. With this in mind, we launch the journal of big data research which aims to promote and communicate advances big data research by providing a fast, high quality and multi-disciplinary forum for researchers, practitioners and policy makers from e very many different communities working on, and with this topic.The journal is also organized in a multi-disciplinary way. To form the editorial board, we invite scholars from a variety of elds or mmunities. The journal will accept papers on foundational aspects and common technologies in dealing with big data, as well as papers specic platforms and technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between lds, and to showcase the benets of data driven research, papers demonstrating applications of big data in domains as diverse as eoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and ug discovery, digital libraries and scientic publications, security and government will also be considered. Occasionally the journal may blish white papers on policies, standards and best practices. We have also organized a number of theme issues for inter-disciplinary udy such as Big Geo Data, Big Data Meets Economics, Big Data in Biology, Big Data in Finance, etc. We believe this journal will become high-quality forum for big data and data science.

Zhaohui WuCollege of Computer Science, Zhejiang University, China

Ooi Beng ChinSchool of Computing, National University of Singapore, Singapore

tp://dx.doi.org/10.1016/j.bdr.2014.08.00214-5796/ 2014 Published by Elsevier Inc.

From Big Data to Data Science: A Multi-disciplinary Perspective