ACM Transactions on Knowledge Discovery from Data
Note: The following journal information is for reference only. Please check the journal website for updated information prior to submission.
Journal Title
ACM Transactions on Knowledge Discovery from Data
ACM T KNOWL DISCOV D
ISSN / eISSN
1556-4681
Aims and Scope
TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.
Subject Area
COMPUTER SCIENCE, INFORMATION SYSTEMS
COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
7.70
View Trend
CiteScore Ranking
| Category | Quartile | Rank |
|---|---|---|
| Computer Science - General Computer Science | Q1 | #30/233 |
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 | Q3 |
| COMPUTER SCIENCE, SOFTWARE ENGINEERING - SCIE | Q2 |
H-index
44
Country/Area of Publication
UNITED STATES
Publisher
Association for Computing Machinery (ACM)
Year Publication Started
2006
Annual Article Volume
100
Open Access
NO
Contact
2 PENN PLAZA, STE 701, NEW YORK, USA, NY, 10121-0701
Verified Reviews
2022.4.14 R1 (Major Revision)
2022.5.24 R1 submit
2022.7.19 R2 (Minor Revision)
2022.8.2 R2 submit
2022.9.21 accept
First review: Four reviewers, one acceptance, one minor revision, two major revisions.
Second review: Originally four reviewers, three acceptances, one minor revision. New reviewer suggests minor revisions.
Finally, all reviewers accept.
The field of graph neural networks for data mining is strict in terms of reviewing, and the expert opinions are professional. However, the review process is quite long. During the three rounds of review, three reminders were sent. After a reminder, there would usually be a response within a week, and the results would be available after some time (whether the reminder was effective or it was just luck?).
It feels like the classification of data mining by the Chinese Academy of Sciences is not very friendly. TKDE is only in the second tier, and TKDD is in the third tier...
2019-5-25 major revision
2019-7-6 revision submitted
2019-9-1 minor revision
2019-9-11 revision submitted
2019-10-2 accepted
Translation:
2019-2-13 submitted
2019-5-25 major revision
2019-7-6 revision submitted
2019-9-1 minor revision
2019-9-11 revision submitted
2019-10-2 accepted
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