Journal Information
Big Data Research
http://www.elsevier.com/journals/big-data-research/2214-5796
Publisher:
ELSEVIER
ISSN:
2214-5796
Viewed:
2474
Tracked:
2

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Call For Papers
The journal aims to promote and communicate advances in Big Data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.

The journal will accept papers on foundational aspects in dealing with Big Data, as well as papers on specific Platforms and Technologies used to deal with Big Data.

To foster interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e–Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered.

The journal may publish whitepapers on policies, standards and best practices.
Last updated by Dou Sun in 2013-11-02
Special Issues
Special Issue on Big Data and Smart Cities
Submission Date: 2017-08-01

Introduction A smart city integrates information and communication technologies, as well as Internet of Things (IoT) solutions to reduce costs and resource consumption, enhance performance, and connect and engage more effectively and actively with its citizens. This vast and semi-structured collection of city and citizen-related data provides many opportunities for the development of smart city applications building on big data technologies. The Smart City Big Data special issue aims to publish work on multidisciplinary research spanning across the of computer science and engineering, environmental studies, urban planning and development, social sciences and industrial engineering on technologies, case studies, novel approaches, and visionary ideas related to data-driven innovative solutions and big data-powered applications to cope with the real world challenges for building smart cities. Topics for the special issue: Topics of interest include, but are not limited to: Big data collection and analysis for smart and connected communities Urban computing and big data analytics Social computing big data and networks for smart cities Urban planning big data evaluation and assessment Smart city big data governance and management Data mining and machine learning for smart cities Smart city transportation big data and analytics Big data sensing and IoT frameworks and infrastructures Big data infrastructures and warehouse for smart cities Smart building big data evaluation Big data modeling and frameworks for smart cities Big data security and privacy for smart cities Big data based city environment monitor, analytics and prediction Smart city open data Case Studies and Innovative Applications In addition to this brief list of possible topics, we will welcome submissions on other topics addressing smart city big data and applications. We will seek papers with conceptual and theoretical contributions and are also open to papers documenting interesting and important effects with a plausible theory to explain these effects in consumer behavior contexts. Paper Submission Format and Guidelines All submitted papers must be clearly written in English and must contain only original work, which has not been published by, or is currently under review for, any other journal, conference, symposium, or workshop. Submissions are expected to not exceed 30 pages (including figures, tables, and references) in the journal's single-column format using 11 point font. Detailed submission guidelines are available under "Guide for Authors" at: http://www.journals.elsevier.com/big-data-research/ All manuscripts and any supplementary material should be submitted through the Elsevier Editorial System (EES). The authors must select "SI: Smart City Big Data" as Article Type when they reach the Article Type step in the submission process. The EES website is located at: http://ees.elsevier.com/bdr All papers will be peer-reviewed by at least three independent reviewers. Requests for additional information should be addressed to the guest editors. Guest Editors Magdalini Eirinaki, San Jose State University, CA, USA Jerry Gao, San Jose State University, CA, USA Latifur Khan, University of Texas at Dallas, USA Sourav Mazumder, IBM, USA Aikaterini Potika, San Jose State University, CA, USA Contact information: Magdalini Eirinaki, magdalini.eirinaki@sjsu.edu Important Dates Paper submission due date: August 1, 2017 Notification of acceptance: November 15, 2017 Revised version due date: December 15, 2017 Camera-ready copy due date: February 15, 2017 Expected publication in Big Data Research special issue: April 2018
Last updated by Dou Sun in 2017-03-04
Special Issue on Hybrid Evolutionary and Swarm Techniques for Big Data Analytics and Applications
Submission Date: 2017-11-30

TOPIC SUMMARY: Rapid growth of data has led to the urgent need to develop effective and efficient big data analytics techniques for industries and academia to discover information or knowledge from big data. Big data analytics concerns modern statistical and machine learning techniques to analyze huge amounts of data. Challenging issues in Big Data Analytics particularly include the high dimensionality of data and multiple objectives of the problems under study, in addition to the conventional 5Vs, i.e., large scale of data (Volume), multiple sources of data (Variety), rapid growth of data (Velocity), quality of data (Veracity), and usefulness of data (Value). With powerful search capabilities for optimization, Evolutionary and Swarm Algorithms (ESA)have the potential to address the above challenges in the big data analytics today. Combined ESA with other conventional and recent statistical and machine learning techniques, development of hybrid ESA techniques for Big Data Analytics is a fast-growing and promising multidisciplinary research area. Hybrid ESA can be developed, with the foundations of ESA such as Genetic Algorithms, Differential Evolution, Particle Swarms, Ant Colony, Memetic Computing, Bacterial Foraging, Artificial Bees, and their hybrids, along with other general machine learning methods, for clustering, classification, regression, case-based reasoning, decision making methods, modelling. This special issue aims to bring together academia and industry experts to report on the recent developments on hybrid evolutionary and swarm techniques for solving specific challenges of big data analytics from various industries. Relevant areas of interests include (but are not limited to) the following: Hybrid analytics techniques with ESA for Big Data Analytics (BDA): Clustering with ESA for Big Data Analytics Regression with ESA for Big Data Analytics Classification with ESA for Big Data Analytics Association learning with ESA for Big Data Analytics Reinforcement learning with ESA for Big Data Analytics Fuzzy systems with ESA for Big Data Analytics Decision and recommendation algorithms with ESA for Big Data Analytics Knowledge based systems with ESA for Big Data Analytics Neural network algorithms with ESA for Big Data Analytics, etc Big data analytics applications using hybrid ESA techniques in: Industrial systems Energy research Social network analysis Operations research and decision sciences Financial and economic analysis Internet computing Image processing Bioinformatics and computational biology Medicine and healthcare Environment and urban design, etc In addition to the normal submissions, the special issue also considers to select some of the best papers (substantially extended and re-reviewed) from the special session in 2017 International Conference on Big Data Analytics and Business Intelligence (ICBDBI 2017) available here: http://www.xjtlu.edu.cn/en/icbdbi/ (to be updated) IMPORTANT DATES: Submission Deadline: 30 Nov 2017 Author Notification: 25 Feb 2018 Revised Manuscript Due: 25 April 2018 Notification of Acceptance: 30 May 2018 Final Manuscript Due: 20 June 2018 Tentative Publication Date: Sep 2018
Last updated by Dou Sun in 2017-03-04
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