IEEE Transactions on Big Data

Journal Title
IEEE Transactions on Big Data

IEEE T BIG DATA

ISSN / eISSN
2332-7790 / 2332-7790
Subject Area

COMPUTER SCIENCE, THEORY & METHODS

COMPUTER SCIENCE, INFORMATION SYSTEMS

CiteScore
9.70 View Trend
CiteScore Ranking
Category Quartile Rank
Computer Science - Information Systems Q1 #47/379
Computer Science - Information Systems and Management Q1 #18/140
Web of Science Core Collection
Science Citation Index Expanded (SCIE) Social Sciences Citation Index (SSCI)
Indexed -
Category (Journal Citation Reports 2023) Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS - SCIE Q1
COMPUTER SCIENCE, THEORY & METHODS - SCIE Q1
Country/Area of Publication
UNITED STATES
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Annual Article Volume
107
Open Access
NO
Contact
445 HOES LANE, PISCATAWAY, USA, NJ, 08855-4141
Verified Reviews
Note: Verified reviews are sourced from across review platforms and social media globally.
I'll share my experience of rejection. To be honest, I have had numerous articles rejected myself and it happens quite often. But the peer review in this case was beyond belief, and I truly have no words. Suddenly, I understand what others have said, that journal editors reject based on their own preferences.

I first submitted in December of the 21st year, and after two and a half months, I received the first review. The review included detailed comments from each reviewer, with two major revisions and one minor revision. The main feedback was that the data was insufficient. The Associate Editor (AE) gave a "reject and resubmit" decision, asking me to supplement the data. So, I added new data in April, which was more than twice the amount of the previous data.

In early April, I submitted the revised article for the second review, and received the feedback on July 4th. It included one major revision, one minor revision, and an acceptance. However, the AE rejected it, stating a lack of novelty. The AE said that all the reviewers pointed out that my method was too simple and lacked complexity, leaning more towards engineering.

Firstly, the idea I proposed was the first of its kind in the field. Secondly, the method itself focuses on information fusion analysis. As a data mining article, it naturally includes a significant amount of analysis, including engineering-oriented analysis. This can be seen in many articles published in TKDD and TKDE.

Furthermore, I compared my method with eight other methods from recent years (2019-2021) which were published in journals like TPAMI and DMKD. My method yielded the best results. Isn't it a contribution to have a simple and effective approach?
2022-07-05
I'm sorry, but I am unable to directly translate a webpage. However, I can provide you with a general description of the webpage you provided.

The link you shared directs to the IEEE Xplore Digital Library, specifically to a page displaying the recent issue of a publication. The publication is associated with the IEEE (Institute of Electrical and Electronics Engineers) and has a unique number identified as "6687317." The content of the page is likely to be related to research articles, papers, or other scientific content within the field of electrical engineering and computer science. To access the specific content and understand its details, you may need to visit the page and use the available features and options provided by the IEEE Xplore Digital Library.
2022-03-27

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