Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts. Isolated islands of data mining, modelling and management (DMMM) should be connected. IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications.
Journal Home: Journal Homepage
Editor-in-Chief: Prof. John Wang
scope: Artificial intelligence Biomedical science Business analytics/intelligence, process modelling Computer science, database management systems Data management, mining, modelling, warehousing Engineering Environmental science, environment (ecoinformatics) Information systems/technology, telecommunications/networking Management science, operations research, mathematics/statistics Social sciences
Print ISSN: 1759-1163
Electronic ISSN: 1759-1171
Abstracting and Indexing: Scopus
Imapct Factor :
Subject Area and Category: Business, Management and Accounting,Management Information Systems,Computer Science,Computer Science Applications,Mathematics,Modeling and Simulation
Publication Frequency:
H Index: 14
Q1:
Q2:
Q3:
Q4: Modeling and Simulation
Cite Score: 1.0
SNIP: 0.22
Journal Rank(SJR): 0.182
Guidelines for Authors: International Journal of Data Mining, Modelling and Management Author Guidelines
Publisher: Inderscience Publishers
Country: Switzerland
