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  1. The age of big data is now coming. But the traditional data analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to effic...

    Authors: Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos
    Citation: Journal of Big Data 2015 2:21
  2. The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These s...

    Authors: Peter O’Donovan, Kevin Leahy, Ken Bruton and Dominic T. J. O’Sullivan
    Citation: Journal of Big Data 2015 2:20
  3. For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have eme...

    Authors: João Ricardo Lourenço, Bruno Cabral, Paulo Carreiro, Marco Vieira and Jorge Bernardino
    Citation: Journal of Big Data 2015 2:18
  4. W e h a v e e n t e r e d t h e b i g data age. Knowledge extraction from massive data is becoming more and more urgent. MapReduce provides a feasible framework for programming machine learning algorithms in M...

    Authors: Xuan Liu, Xiaoguang Wang, Stan Matwin and Nathalie Japkowicz
    Citation: Journal of Big Data 2015 2:14
  5. Intuitive formulation of informative and computationally-efficient queries on big and complex datasets present a number of challenges. As data collection is increasingly streamlined and ubiquitous, data explor...

    Authors: Syed S Husain, Alexandr Kalinin, Anh Truong and Ivo D Dinov
    Citation: Journal of Big Data 2015 2:13
  6. Community structure is thought to be one of the main organizing principles in most complex networks. Big data and complex networks represent an area which researchers are analyzing worldwide. Of special intere...

    Authors: Pravin Chopade and Justin Zhan
    Citation: Journal of Big Data 2015 2:11

    The Erratum to this article has been published in Journal of Big Data 2015 2:19

  7. Automated workflows are the key concept of big data pipelines in science, engineering and enterprise applications. The performance analysis of automated workflows is an important topic of the continuous improv...

    Authors: Andreas Kempa-Liehr
    Citation: Journal of Big Data 2015 2:10
  8. The promise of Big Biomedical Data may be offset by the enormous challenges in handling, analyzing, and sharing it. In this paper, we provide a framework for developing practical and reasonable data sharing po...

    Authors: Arthur W Toga and Ivo D Dinov
    Citation: Journal of Big Data 2015 2:7
  9. Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of s...

    Authors: Xing Fang and Justin Zhan
    Citation: Journal of Big Data 2015 2:5
  10. The paper discusses the shift in the computing paradigm and the programming model for Big Data problems and applications. We compare DataFlow and ControlFlow programming models through their quantity and quali...

    Authors: Nemanja Trifunovic, Veljko Milutinovic, Jakob Salom and Anton Kos
    Citation: Journal of Big Data 2015 2:4
  11. Intrusion Detection has been heavily studied in both industry and academia, but cybersecurity analysts still desire much more alert accuracy and overall threat analysis in order to secure their systems within ...

    Authors: Richard Zuech, Taghi M Khoshgoftaar and Randall Wald
    Citation: Journal of Big Data 2015 2:3
  12. The ability to detect and process anomalies for Big Data in real-time is a difficult task. The volume and velocity of the data within many systems makes it difficult for typical algorithms to scale and retain ...

    Authors: Michael A Hayes and Miriam AM Capretz
    Citation: Journal of Big Data 2015 2:2
  13. Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific info...

    Authors: Maryam M Najafabadi, Flavio Villanustre, Taghi M Khoshgoftaar, Naeem Seliya, Randall Wald and Edin Muharemagic
    Citation: Journal of Big Data 2015 2:1
  14. Molecular Simulation (MS) is a powerful tool for studying physical/chemical features of large systems and has seen applications in many scientific and engineering domains. During the simulation process, the ex...

    Authors: Anand Kumar, Vladimir Grupcev, Meryem Berrada, Joseph C Fogarty, Yi-Cheng Tu, Xingquan Zhu, Sagar A Pandit and Yuni Xia
    Citation: Journal of Big Data 2014 2:9
  15. The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data a...

    Authors: Dilpreet Singh and Chandan K Reddy
    Citation: Journal of Big Data 2014 2:8
  16. I describe a research agenda for data science based on a decade of research and operational work in data-intensive systems at NASA, the University of Southern California, and in the context of open source work...

    Authors: Chris A Mattmann
    Citation: Journal of Big Data 2014 1:6
  17. The amount of data produced within Health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. In addition, this informat...

    Authors: Matthew Herland, Taghi M Khoshgoftaar and Randall Wald
    Citation: Journal Of Big Data 2014 1:2

Annual Journal Metrics

  • 2022 Citation Impact
    8.1 - 2-year Impact Factor
    5.095 - SNIP (Source Normalized Impact per Paper)
    2.714 - SJR (SCImago Journal Rank)

    2023 Speed
    56 days submission to first editorial decision for all manuscripts (Median)
    205 days submission to accept (Median)

    2023 Usage 
    280 Altmetric mentions