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.

Topics covered include

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
Business/economics, (computational) finance
Healthcare, medicine, pharmaceuticals
(Computational) chemistry, biology (bioinformatics)
Sustainable mobility systems, intelligent transportation systems
National security

Objectives

IJDMMM aims to provide a professional forum for formulating, discussing and disseminating these solutions, which relate to the design, development, deployment, management, measurement, and adjustment of data warehousing, data mining, data modelling, data management, and other data analysis techniques. They should form a common ground on which a data chain management system can be built, shared and supported by professionals from different disciplines.

IJDMMM provides a communication channel between practitioners and academics to discuss problems, challenges and opportunities in all aspects of data mining, data modelling, data analysis, and data management. The process of knowledge creation can include multiple components, including data acquisition/collection, data accumulation, data maturation, data selection and refining, data storage and retrieval, data pre-processing, data analysis and validation, data maintenance and data presentation, data warehousing, data mining and/or modelling, and information extraction. Therefore, data chain management cannot be isolated, separated, broken, or ignored. It is an integrated and interconnected process.

Readership

Academics and libraries in the field
Consultants
Data analysts and data miners
Database administrators
Revenue and/or risk managers
Applied statisticians
Operation researchers
Management scientists
Marketing researchers and managers
Policy makers
Strategic planners
Educators and graduate students
Problem solvers and decision makers

Contents

IJDMMM publishes research papers, innovative ideas, reviews, surveys, debates, reports, case studies, position notes, practice comments, book reviews, commentaries, and news. Special issues devoted to important topics in data mining, modelling and management will occasionally be published.