International Journal of Data Modelling

and Knowledge Management

ISSN: 2249-0507

Editor in Chief

J. Wang
Montclair State University, USA

Indexing : The journal is index in Worldcat, Publons (Clarivate Analytics

Click here to view the complete Editorial Board Members 

ISSN: 2249-0507 

Open AccessThis journal also publishes Open Access articles

Aim and Scope  

Data Modelling and Knowledge Discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting
useful information from the rapidly growing volumes of digital data.

This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the workshop by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.

Data Modelling foundations

Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining, Pre-processing techniques, Visualization, Security and information hiding in data mining

Data Modelling Applications

Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining

Knowledge Processing

Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages

Topics covered include

Artificial intelligence

Biomedical science

Business analytics/intelligence, process modelling

Computer science, database management systems

Data management, mining, modelling, warehousing


Environmental science, environment (ecoinformatics)

Information systems/technology, telecommunications/networking

Management science, operations research, mathematics/statistics

Social sciences

Business/economics, (computational) finance

Healthcare, medicine, pharmaceuticals

(Computational) chemistry, biology (bioinformatics)

Sustainable mobility systems, intelligent transportation systems

National security

International Journal of Data Modelling and Knowledge Management (IJDMKM) currently has an acceptance rate of 33%(2020). The average time between submission and final decision is 1 month and the average time between acceptance and publication is 3 to 4 month. 


Abstract Guidelines: Abstracts must include sufficient information for reviewers to judge the nature and significance of the topic, the adequacy of the investigative strategy, the nature of the results, and the conclusions. The abstract should summarize the substantive results of the work and not merely list topics to be discussed. An abstract is an outline/brief summary of your paper and your whole project. It should have an intro, body and conclusion. It is a well-developed paragraph, should be exact in wording, and must be understandable to a wide audience. Abstracts should be no more than 250 words, formatted in Microsoft Word, and single-spaced, using size 12 Times New Roman font. Abstracts highlight major points of your research and explain why your work is important; what your purpose was, how you went about your project, what you learned, and what you concluded. If your title includes scientific notation, Greek letters, bold, italics, or other special characters/symbols, do make sure they appear correctly. 

Submission: Authors are requested to submit their papers electronically to  

Int. J. Data Model. Knowl. Manag

Subject: Data Mining, Data Management, Mining, Modelling, Warehousing, Knowledge Management 

Readership: The journal is useful for the Economists, Policy Makers, Academicians, Universities, Researchers and related bodies.

Article Processing Charges: The journal publish articles in Open Access Model. In this Open Access model, the publication cost should be covered by the author's institution or research funds. These Open Access charges replace subscription charges and allow the publishers to give the published material away for free to all interested online visitors.

Frequency: Two issues per year. 

License: Creative Commons 3.0

Review Process:  This journal uses double-blind review, which means that both the reviewer and author identities are concealed from the reviewers, and vice versa, throughout the review process. The articles is submitted to minimum 3 reviewers specialize on the topic for their reviews.    

Corrections to Published Work: 

Honest errors are a part of research and publishing and require publication of a correction when they are detected. We expect authors to inform the Journal’s Editor of any errors of fact they have noticed or been informed of in their article once published. Corrections are made at the journal’s discretion. The correction procedure depends on the publication stage of the article, but in all circumstances a correction notice is published as soon as possible. Details can be found on the Call for Papers section of the journal website. 


Retractions are considered by journal editors in cases of evidence of unreliable data or findings, plagiarism, duplicate publication, and unethical research. We may consider an expression of concern notice if an article is under investigation. All retraction notices will explain why the article was retracted. The retraction procedure depends on the publication stage of the article.

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Editorial Board 


Instructions to Authors

Vol. 8 No. 1 (June, 2023)          

Vol. 7 No. 1 (June, 2022)             Vol. 7 No. 2 (December, 2022)

Vol. 6 No. 1 (June, 2021)          Vol. 6 No. 2 (December, 2021)

Vol. 5 No. 1 (June, 2020)         Vol. 5 No. 2 (December, 2020)

Vol. 4 No. 1 (June, 2019)          Vol. 4 No. 2 (December, 2019)

Vol. 3 No. 1 (June, 2018)        Vol. 3 No. 2 (December, 2018)

Vol. 2 No. 1 (June, 2017)          Vol. 2 No. 2 (December, 2017)

Vol. 1 No. 1 (June, 2016)           Vol. 1 No. 2 (December, 2016)