IEEE Transactions on Knowledge and Data Engineering

Top Research Topics at IEEE Transactions on Knowledge and Data Engineering?

IEEE Transactions on Knowledge and Data Engineering tackles a plethora of topics, such as Data mining, Artificial intelligence, Theoretical computer science, Machine learning and Information retrieval. Topics in Data mining explored in IEEE Transactions on Knowledge and Data Engineering were investigated in conjunction with research in Algorithm design, Set (abstract data type), Cluster analysis, Search engine indexing and Data set. Correlation clustering and Fuzzy clustering are Cluster analysis topics of special interest in the journal.

Artificial intelligence research featured in IEEE Transactions on Knowledge and Data Engineering incorporates concerns from various other topics such as Data modeling, Natural language processing and Pattern recognition. IEEE Transactions on Knowledge and Data Engineering explores topics in Theoretical computer science which can be helpful for research in disciplines like Algorithm, Relational database, Scalability and Graph. The concepts on Graph presented in IEEE Transactions on Knowledge and Data Engineering can also apply to other research fields, including Graph theory and Graph (abstract data type).

Research on Information retrieval presented in the journal focuses, in particular, on Query language and Search engine. IEEE Transactions on Knowledge and Data Engineering focuses on Query language research which is adjacent to topics in Query optimization. The work on Query optimization addressed in the journal expands to the thematically related Web query classification.

• Data mining (27.76%)
• Artificial intelligence (27.68%)
• Theoretical computer science (15.79%)

What are the most cited papers published in the journal?

• A Survey on Transfer Learning (11128 citations)
• Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (8102 citations)
• Learning from Imbalanced Data (4898 citations)

Research areas of the most cited articles at IEEE Transactions on Knowledge and Data Engineering:

The journal articles focus largely on the fields of Data mining, Artificial intelligence, Machine learning, Cluster analysis and Information retrieval. While the primary focus in the journal articles is Data mining, they also dissect topics surrounding Data set and Algorithm as a whole. The published papers facilitate discussions on Artificial intelligence that incorporate concepts from other fields like Natural language processing and Pattern recognition.

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 journal was organized to reinforce research efforts on Artificial intelligence, Machine learning, Theoretical computer science, Data mining and Graph (abstract data type). Some problems in Artificial intelligence that were presented in it overlapped with concepts under Task (project management) and Pattern recognition. The presented research on Pattern recognition deals specifically with Cluster analysis but it also addresses topics in Algorithm.

Data modeling and Task analysis are some topics wherein Machine learning research discussed in it have an impact. It facilitates discussions on Theoretical computer science that incorporate concepts from other fields like Scalability, Embedding, Structure (mathematical logic), Graph and Node (networking). Recommender system research presented falls under the umbrella topic of Information retrieval.

The most cited articles from the last journal are:

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (154 citations)
• A Survey of Utility-Oriented Pattern Mining (74 citations)
• EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction (66 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 IEEE Transactions on Knowledge and Data Engineering (based on the number of publications) are:

• Philip S. Yu (92 papers) published 11 papers at the last edition, 5 more than at the previous edition,
• Lei Chen (69 papers) published 13 papers at the last edition, 3 more than at the previous edition,
• Ming-Syan Chen (59 papers) published 2 papers at the last edition the same number as at the previous edition,
• Jiawei Han (52 papers) published 2 papers at the last edition, 1 more than at the previous edition,
• Xiaofang Zhou (52 papers) published 9 papers at the last edition, 6 less 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 IEEE Transactions on Knowledge and Data Engineering (based on the number of publications) are:

• Hong Kong University of Science and Technology (157 papers) published 22 papers at the last edition, 10 more than at the previous edition,
• Tsinghua University (153 papers) published 39 papers at the last edition, 11 more than at the previous edition,
• IBM (151 papers) published 6 papers at the last edition, 2 more than at the previous edition,
• National University of Singapore (142 papers) published 18 papers at the last edition, 3 less than at the previous edition,
• The Chinese University of Hong Kong (120 papers) published 16 papers at the last edition, 7 more 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, 17.12% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 30.06% were posted by at least one author from the top 10 institutions publishing in the journal. Another 10.84% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.13% of all publications and 34.97% 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.

Exploring Career Pathways in Data Science

While this article offers an insightful exploration of topics tackled in the IEEE Transactions on Knowledge and Data Engineering, it is beneficial to consider the professional applications of these ideas in real-life scenarios. For instance, for those considering a career in research or academia, understanding these topics can act as a stepping stone towards contributing meaningfully in the field of knowledge and data engineering.

In particular, shifting our focus to the education sector, understanding these concepts can enable individuals to become effective educators in this realm. One intriguing career path to consider would be teaching at elementary schools, which requires a comprehensive understanding of these topics to shape young minds.

The process of transitioning into this profession in different states can differ significantly. Thus, to help aspiring teachers navigate through this process, we provide a link to an informative article that elaborately provides insights on how to become an elementary school teacher in North Carolina. This article provides insights on the steps towards acquiring the necessary credentials for teaching in elementary schools.

By exploring these career pathways, one can align their understanding and skills in knowledge and data engineering with their career goals, ultimately contributing to the development and dissemination of these significant concepts.