Example of Journal of Big Data format
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Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format Example of Journal of Big Data format
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recommended Recommended

Journal of Big Data — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Information Systems #30 of 329 down down by 5 ranks
Information Systems and Management #13 of 125 down down by 3 ranks
Computer Networks and Communications #34 of 334 down down by 11 ranks
Hardware and Architecture #18 of 157 down down by 5 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 316 Published Papers | 2714 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 10/06/2020
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CiteRatio: 13.2
SJR: 1.207
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CiteRatio: 5.0
SJR: 0.445
SNIP: 1.076

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

8.6

41% from 2019

CiteRatio for Journal of Big Data from 2016 - 2020
Year Value
2020 8.6
2019 6.1
2018 10.6
2017 7.3
2016 4.6
graph view Graph view
table view Table view

1.031

11% from 2019

SJR for Journal of Big Data from 2016 - 2020
Year Value
2020 1.031
2019 0.925
2018 1.124
2017 1.143
2016 0.83
graph view Graph view
table view Table view

3.478

39% from 2019

SNIP for Journal of Big Data from 2016 - 2020
Year Value
2020 3.478
2019 2.501
2018 4.512
2017 4.649
2016 4.885
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 41% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 11% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 39% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Big Data

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Springer

Journal of Big Data

The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing...... Read More

Communications Engineering, Networks

Information Systems and Management

Computer Networks and Communications

Hardware and Architecture

Decision Sciences

i
Last updated on
10 Jun 2020
i
ISSN
1606-8610
i
Impact Factor
Very Low - 0.024
i
Acceptance Rate
Not provided
i
Frequency
Not provided
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
White faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
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Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM (1982) Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B 25(7):4515–4532, URL 10.1103/PhysRevB.25.4515

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/S40537-019-0197-0
A survey on Image Data Augmentation for Deep Learning
Connor Shorten1, Taghi M. Khoshgoftaar1
06 Jul 2019 - Journal of Big Data

Abstract:

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunatel... Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augmentation algorithms discussed in this survey include geometric transformations, color space augmentations, kernel filters, mixing images, random erasing, feature space augmentation, adversarial training, generative adversarial networks, neural style transfer, and meta-learning. The application of augmentation methods based on GANs are heavily covered in this survey. In addition to augmentation techniques, this paper will briefly discuss other characteristics of Data Augmentation such as test-time augmentation, resolution impact, final dataset size, and curriculum learning. This survey will present existing methods for Data Augmentation, promising developments, and meta-level decisions for implementing Data Augmentation. Readers will understand how Data Augmentation can improve the performance of their models and expand limited datasets to take advantage of the capabilities of big data. read more read less

Topics:

Overfitting (56%)56% related to the paper, Deep learning (55%)55% related to the paper, Big data (52%)52% related to the paper, Convolutional neural network (52%)52% related to the paper
View PDF
1,965 Citations
open accessOpen access Journal Article DOI: 10.1186/S40537-016-0043-6
A survey of transfer learning
Karl R. Weiss1, Taghi M. Khoshgoftaar1, Dingding Wang1
28 May 2016 - Journal of Big Data

Abstract:

Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in som... Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the input feature space and data distribution characteristics are the same. However, in some real-world machine learning scenarios, this assumption does not hold. There are cases where training data is expensive or difficult to collect. Therefore, there is a need to create high-performance learners trained with more easily obtained data from different domains. This methodology is referred to as transfer learning. This survey paper formally defines transfer learning, presents information on current solutions, and reviews applications applied to transfer learning. Lastly, there is information listed on software downloads for various transfer learning solutions and a discussion of possible future research work. The transfer learning solutions surveyed are independent of data size and can be applied to big data environments. read more read less

Topics:

Active learning (machine learning) (68%)68% related to the paper, Inductive transfer (67%)67% related to the paper, Instance-based learning (66%)66% related to the paper, Multi-task learning (66%)66% related to the paper, Algorithmic learning theory (63%)63% related to the paper
View PDF
1,650 Citations
open accessOpen access Journal Article DOI: 10.1186/S40537-014-0007-7
Deep learning applications and challenges in big data analytics
24 Feb 2015 - Journal of Big Data

Abstract:

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 information, which can contain useful information about problems such as national intelligence, cyber security, fraud dete... 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 information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning. read more read less

Topics:

Analytics (69%)69% related to the paper, Big data (67%)67% related to the paper, Business intelligence (59%)59% related to the paper, Active learning (58%)58% related to the paper, Raw data (53%)53% related to the paper
View PDF
1,293 Citations
open accessOpen access Journal Article DOI: 10.1186/S40537-019-0192-5
Survey on deep learning with class imbalance
Justin M. Johnson1, Taghi M. Khoshgoftaar1
01 Mar 2019 - Journal of Big Data

Abstract:

The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, hi... The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g., fraud detection and cancer detection. Moreover, highly imbalanced data poses added difficulty, as most learners will exhibit bias towards the majority class, and in extreme cases, may ignore the minority class altogether. Class imbalance has been studied thoroughly over the last two decades using traditional machine learning models, i.e. non-deep learning. Despite recent advances in deep learning, along with its increasing popularity, very little empirical work in the area of deep learning with class imbalance exists. Having achieved record-breaking performance results in several complex domains, investigating the use of deep neural networks for problems containing high levels of class imbalance is of great interest. Available studies regarding class imbalance and deep learning are surveyed in order to better understand the efficacy of deep learning when applied to class imbalanced data. This survey discusses the implementation details and experimental results for each study, and offers additional insight into their strengths and weaknesses. Several areas of focus include: data complexity, architectures tested, performance interpretation, ease of use, big data application, and generalization to other domains. We have found that research in this area is very limited, that most existing work focuses on computer vision tasks with convolutional neural networks, and that the effects of big data are rarely considered. Several traditional methods for class imbalance, e.g. data sampling and cost-sensitive learning, prove to be applicable in deep learning, while more advanced methods that exploit neural network feature learning abilities show promising results. The survey concludes with a discussion that highlights various gaps in deep learning from class imbalanced data for the purpose of guiding future research. read more read less

Topics:

Feature learning (55%)55% related to the paper, Deep learning (55%)55% related to the paper, Class (computer programming) (52%)52% related to the paper, Artificial neural network (52%)52% related to the paper, Big data (51%)51% related to the paper
View PDF
546 Citations
open accessOpen access Journal Article DOI: 10.1186/S40537-015-0030-3
Big data analytics: a survey
Chun-Wei Tsai1, Chin-Feng Lai2, Han-Chieh Chao3, Han-Chieh Chao4, Han-Chieh Chao1, Athanasios V. Vasilakos5
01 Oct 2015 - Journal of Big Data

Abstract:

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 efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data.... 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 efficiently analyze big data and how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. Some important open issues and further research directions will also be presented for the next step of big data analytics. read more read less

Topics:

Analytics (76%)76% related to the paper, Big data (67%)67% related to the paper, Semantic analytics (65%)65% related to the paper, Software analytics (64%)64% related to the paper, Business analytics (63%)63% related to the paper
View PDF
457 Citations
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Frequently asked questions

1. Can I write Journal of Big Data in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Big Data guidelines and auto format it.

2. Do you follow the Journal of Big Data guidelines?

Yes, the template is compliant with the Journal of Big Data guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Big Data?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Big Data citation style.

4. Can I use the Journal of Big Data templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Big Data.

5. Can I use a manuscript in Journal of Big Data that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Big Data that you can download at the end.

6. How long does it usually take you to format my papers in Journal of Big Data?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Journal of Big Data.

7. Where can I find the template for the Journal of Big Data?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Big Data's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Journal of Big Data's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Journal of Big Data an online tool or is there a desktop version?

SciSpace's Journal of Big Data is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Journal of Big Data?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Journal of Big Data?”

11. What is the output that I would get after using Journal of Big Data?

After writing your paper autoformatting in Journal of Big Data, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Big Data's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Big Data?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Big Data. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Big Data?

The 5 most common citation types in order of usage for Journal of Big Data are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Big Data?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Journal of Big Data's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Journal of Big Data in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Big Data Endnote style according to Elsevier guidelines.

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