Big Data Research

Top Research Topics at Big Data Research?

Big Data Research covers a variety of subjects, including Big data, Data science, Data mining, Artificial intelligence and Scalability. While it focused on Big data, it was also able to explore topics like Theoretical computer science, Distributed computing, Computer security, Analytics and Cloud computing. The journal links adjacent topics like Data science with The Internet.

Discussions in the journal are anchored in the subject of Data mining and the similar topic of Cluster analysis. While Artificial intelligence is the focus of it, it also provided insights into the studies of Machine learning and Pattern recognition.

• Big data (90.33%)
• Data science (26.59%)
• Data mining (16.01%)

What are the most cited papers published in the journal?

• Significance and Challenges of Big Data Research (417 citations)
• Efficient Machine Learning for Big Data (291 citations)
• Geospatial Big Data (199 citations)

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

The most cited papers tackle a plethora of topics, such as Big data, Data science, Cloud computing, Analytics and Data mining. Scalability, Emerging technologies, The Internet, Exploit and Machine learning are some topics wherein Big data research discussed in the published papers has an impact. While the journal publications focused on Data science, they were also able to explore topics like Return on marketing investment, World Wide Web and Data system.

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

• Artificial intelligence
• Operating system
• Statistics

The previous edition focused in particular on these issues:

The discussions in Big Data Research mainly cover the fields of Big data, Artificial intelligence, Data science, Machine learning and Cloud computing. The tackled Big data research is interrelated with Distributed computing which concerns subjects like Scalability. It connects research in Artificial intelligence with the related topic of Pattern recognition.

Some problems in Data science that were presented in the journal overlapped with concepts under Domain (software engineering), Topic model, Latent Dirichlet allocation, Control (management) and Information system. It holds forums on Cloud computing that merges themes from other disciplines such as Virtual machine, Data processing, Encryption and Distributed database. The study of Robustness (computer science) and how it intertwines with concepts under Data mining were explored in the presented Benchmark (computing) research.

The most cited articles from the last journal are:

• A Framework for Pandemic Prediction Using Big Data Analytics (8 citations)
• Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images (7 citations)
• SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation (6 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 Big Data Research (based on the number of publications) are:

• Xiaoyong Li (4 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Sherif Sakr (4 papers) absent at the last edition,
• MU Shaomin (3 papers) absent at the last edition,
• Xiong Yun (3 papers) absent at the last edition,
• Junqiang Song (3 papers) published 2 papers at the last edition, 1 more than at the previous 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 Big Data Research (based on the number of publications) are:

• Chinese Academy of Sciences (6 papers) published 2 papers at the last edition,
• Aristotle University of Thessaloniki (5 papers) absent at the last edition,
• IBM (5 papers) published 2 papers at the last edition,
• Harbin Institute of Technology (4 papers) absent at the last edition,
• University of Queensland (4 papers) published 4 papers at the last 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, 4.65% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 13.41% were posted by at least one author from the top 10 institutions publishing in the journal. Another 14.63% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.29% of all publications and 53.66% 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 Research

One crucial section that seems to be missing is a discussion of how Big Data research knowledge and skills can open up vocational opportunities for enthusiasts or researchers in this field. With rapid technological advancements and a growing dependence on data-driven decision-making, there is a significant demand for professionals equipped with proficiency in Big Data and related fields such as Artificial intelligence, Machine Learning, Data Mining, and Cloud Computing. In the education sector, for example, roles like teachers who can educate younger generations about this promising field are in demand. If you are interested in teaching positions, particularly at a foundational level such as elementary schooling, you could expand your career horizons to states like Alaska where the emphasis on technical education is growing steadfastly. Here's a useful resource on how to become an elementary teacher in Alaska. Moreover, other professions such as Data Analysts, Data Scientists, AI specialists, and Cloud Computing experts can offer attractive career paths for Big Data researchers. These job roles are not just limited to tech companies, but span across sectors like finance, healthcare, retail, and more that are looking to harness the power of Big Data for business growth. Whether you are an experienced professional looking to shift your career towards Big Data or a student aspiring to enter this field, understanding the Big Data landscape can help you navigate your career pathway more effectively. By staying updated about emerging research topics, notable publications, and top institutions in the field, you can align your professional development with the trends in Big Data research and find the right opportunities.