Journal of Big Data

Top Research Topics at Journal of Big Data?

The journal mainly deals with areas of study such as Big data, Artificial intelligence, Machine learning, Data mining and Data science. Some problems in Big data that were presented in the journal overlapped with concepts under Scalability, Analytics and Process (engineering). The work tackled in it goes beyond the discipline of Artificial intelligence as it also encompasses Pattern recognition.

Machine learning, which encompasses Random forest, Decision tree and Naive Bayes classifier, is the main subject of Journal of Big Data. The study on Data mining presented in the journal intersects with the topics under Cluster analysis. The research on Data science featured in it combines topics in other fields like Field (computer science) and Social media.

Research on Deep learning addressed in Journal of Big Data frequently intersections with the field of Convolutional neural network.

• Big data (42.23%)
• Artificial intelligence (32.72%)
• Machine learning (17.37%)

What are the most cited papers published in the journal?

• A survey on Image Data Augmentation for Deep Learning (1675 citations)
• A survey of transfer learning (1522 citations)
• Deep learning applications and challenges in big data analytics (1081 citations)

Research areas of the most cited articles at Journal of Big Data:

The most cited papers cover a variety of subjects, including Big data, Data science, Artificial intelligence, Machine learning and Analytics. The published articles hold forums on Big data that merge themes from other disciplines such as Computer-integrated manufacturing, Knowledge management, Field (computer science), Social media and Cloud computing. The most cited papers deal with Analytics in conjunction with Business intelligence and similar fields in Active learning.

What topics the last edition of the journal is best known for?

• Artificial intelligence
• Operating system
• Machine learning

The previous edition focused in particular on these issues:

Journal of Big Data aims to foster the development of research in Artificial intelligence, Big data, Machine learning, Computational Science and Engineering and Deep learning. Issues in Artificial intelligence were discussed, taking into consideration concepts from other disciplines like Natural language processing and Pattern recognition. While work presented in it provided substantial information on Pattern recognition, it also covered topics in Genetic algorithm and Task (project management).

It addresses concerns in Big data which are intertwined with other disciplines, such as Graph (abstract data type) and Analytics, Data science. It focuses on Machine learning but the discussions also offer insight into other areas such as Field (computer science) and Intrusion detection system. Social media, Convolutional neural network and Word embedding are some topics wherein Deep learning research discussed in the journal have an impact.

The most cited articles from the last journal are:

• Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (44 citations)
• Deep Learning applications for COVID-19 (28 citations)
• A survey on generative adversarial networks for imbalance problems in computer vision tasks (13 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

Top authors and change over time

The top authors publishing in Journal of Big Data (based on the number of publications) are:

• Taghi M. Khoshgoftaar (27 papers) published 7 papers at the last edition, 2 more than at the previous edition,
• Justin Zhan (12 papers) absent at the last edition,
• Taghi M. Khoshgoftaar (9 papers) published 1 paper at the last edition the same number as at the previous edition,
• Joffrey L. Leevy (9 papers) published 2 papers at the last edition, 2 less than at the previous edition,
• Sachin Kumar (7 papers) absent at the last edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing in Journal of Big Data (based on the number of publications) are:

• Florida Atlantic University (44 papers) published 9 papers at the last edition, 2 more than at the previous edition,
• Islamic Azad University (14 papers) published 6 papers at the last edition, 4 more than at the previous edition,
• Binus University (14 papers) published 8 papers at the last edition, 5 more than at the previous edition,
• Higher Institute for Applied Sciences and Technology (12 papers) published 1 paper at the last edition, 5 less than at the previous edition,
• University of Indonesia (12 papers) published 4 papers at the last edition, 2 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 8.63% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.83% were posted by at least one author from the top 10 institutions publishing in the journal. Another 13.39% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 14.96% of all publications and 48.82% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities In Big Data and Related Fields

A career in Big Data and related fields such as Artificial Intelligence, Machine Learning, Data Mining and Data Science can be rewarding and challenging. The skills you acquire from studying these complex subjects are transferable, as they can be applied in various areas such as software development, research, and information technology. For instance, becoming a teacher in these fields can be an interesting career path to follow. There are many opportunities for career development and making significant contributions to the advancement of knowledge in these rapidly growing fields. To kick start your career journey, a comprehensive guide on how to become a teacher in Virginia outlines the necessary steps and qualifications needed, as this can serve as a model for other regions. Furthermore, continuing from where you already are, to further your career in this field you should consider contributing to platforms such as the Journal of Big Data. Sharing your research findings, theories, or professional experiences on this platform increases your visibility in the field, improves your chances of collaboration and networking, and enhances your expertise capacity. In conclusion, whether you are considering shifting your career path or advancing in it, Big Data and its related fields offer vast opportunities for growth, innovation, and impact. Consider exploring these pathways to discover how you can contribute and benefit from its enormous potential.