International Journal of Data Mining, Modelling and Management

Aims & Scope

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. 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.

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Source: OpenAlex · Note: citations accumulate over time so older years appear higher

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