| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 111 | 318 | 318 | 39 |
IEEE Transactions on Big Data explores disciplines such as Big data, Data mining, Artificial intelligence, Data science and Cloud computing. The work on Big data tackled in it brings together disciplines like Data modeling, Scalability, Database, Distributed computing and Computer security. The study on Data mining presented in the journal intersects with subjects under the field of Cluster analysis.
IEEE Transactions on Big Data explores issues in Artificial intelligence which can be linked to other research areas like Machine learning and Pattern recognition. The majority of Data science studies in IEEE Transactions on Big Data are focused on the subject of Analytics. The study on Cloud computing presented in it intersects with the topics under Encryption.
The journal publications cover a variety of subjects, including Big data, Data mining, Artificial intelligence, Data science and Computer security. The most cited publications address concerns in Big data which are intertwined with other disciplines, such as Metadata, Distributed computing, Information privacy, Information retrieval and Cloud computing. The studies on Artificial intelligence discussed at the most cited articles can also contribute to research in the domains of Field (computer science), Machine learning and Pattern recognition.
IEEE Transactions on Big Data primarily focuses on research topics in Big data, Artificial intelligence, Data mining, Data modeling and Machine learning. IEEE Transactions on Big Data addresses concerns in Big data which are intertwined with other disciplines, such as Scalability, Computer security, Differential privacy, Cloud computing and Data science. It holds forums on Artificial intelligence that merges themes from other disciplines such as Graph (abstract data type) and Pattern recognition.
The study of Data mining encompasses disciplines such as Artificial neural network, as well as fields such as Feature learning, all of which overlap with one another. While the journal focused on Data modeling, it was also able to explore topics like Algorithm, Data visualization and Data analysis. The Machine learning works featured in it incorporate elements from Probabilistic logic, Representation (mathematics) and Similarity (psychology).
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.
The top authors publishing in IEEE Transactions on Big Data (based on the number of publications) are:
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.
Only papers with recognized affiliations are considered
The top affiliations publishing in IEEE Transactions on Big Data (based on the number of publications) are:
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.
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, 6.48% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 29.70% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.92% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 17.82% of all publications and 44.55% were from other institutions.
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.
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.
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:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
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