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.