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- W178353301 abstract "Data mining is one of the most important analysis techniques to auto- matically extract knowledge from large amount of data. Nowadays, data mining is based on low-level specifications of the employed techniques typically bounded to a specific analysis platform. Therefore, data mining lacks a modelling architecture that allows analysts to consider it as a truly software-engineering process. Bearing in mind this situation, we propose a model-driven approach which is based on (i) a conceptual modelling framework for data mining, and (ii) a set of model transforma- tions to automatically generate both the data under analysis (that is deployed via data-warehousing technology) and the analysis models for data mining (tailored to a specific platform). Thus, analysts can concentrate on understanding the analysis problem via conceptual data-mining models instead of wasting efforts on low-level programming tasks related to the underlying-platform technical details. These time consuming tasks are now entrusted to the model-transformations scaffolding. The feasibility of our approach is shown by means of a hypothetical data-mining scenario where a time series analysis is required." @default.
- W178353301 created "2016-06-24" @default.
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- W178353301 date "2008-01-01" @default.
- W178353301 modified "2023-10-08" @default.
- W178353301 title "Integrating the Development of Data Mining and Data Warehouses via Model-driven Engineering" @default.
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