Abstract
Data mining techniques are making their entrance in nowadays companies, allowing business users to take informed decisions based on their available data. However, these business experts usually lack the knowledge to perform the analysis of the data by themselves, which makes it necessary to rely on experts in the field of data mining. In an attempt to solve this problem, we previously studied the definition of domain-specific languages, which allowed to specify data mining processes without requiring experience in the applied techniques. The specification was made through high-level language primitives, which referred only to familiar concepts and terms from the original domain of the data. Therefore, technical details about the mining processes were hidden to the final user. Although these languages present themselves as a promising solution, their development can become a challenging task, incurring in costly endeavours. This work describes a development ecosystem devised for the generation of these languages, starting from a generic perspective that can be specialized into the details of each domain.
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Acknowledgements
This work has been partially funded by the Government of Cantabria (Spain) under the doctoral studentship program from the University of Cantabria, and by the Spanish Government under grant TIN2014-56158-C4-2-P (M2C2).
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de la Vega, A., García-Saiz, D., Zorrilla, M., Sánchez, P. (2017). A Model-Driven Ecosystem for the Definition of Data Mining Domain-Specific Languages. In: Ouhammou, Y., Ivanovic, M., Abelló, A., Bellatreche, L. (eds) Model and Data Engineering. MEDI 2017. Lecture Notes in Computer Science(), vol 10563. Springer, Cham. https://doi.org/10.1007/978-3-319-66854-3_3
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