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International Journal of Data Mining, Modelling and Management

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Publisher:
Inderscience
ISSN:
1759-1163
Viewed:
14353
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Call For Papers

International Journal of Data Mining, Modelling and Management is an academic journal published by Inderscience. (ISSN 1759-1163).

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.
Last updated by Dou Sun on

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