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• W2904194741 abstract "Regression analytics has been the standard approach to modeling the relationship between input and output variables, while recent trends aim to incorporate advanced regression analytics capabilities within data management systems (DMS). Linear regression queries are fundamental to exploratory analytics and predictive modeling. However, computing their exact answers leaves a lot to be desired in terms of efficiency and scalability. We contribute with a novel predictive analytics model and an associated statistical learning methodology, which are efficient, scalable and accurate in discovering piecewise linear dependencies among variables by observing only regression queries and their answers issued to a DMS. We focus on in-DMS piecewise linear regression and specifically in predicting the answers to mean-value aggregate queries, identifying and delivering the piecewise linear dependencies between variables to regression queries and predicting the data dependent variables within specific data subspaces defined by analysts and data scientists. Our goal is to discover a piecewise linear data function approximation over the underlying data only through query–answer pairs that is competitive with the best piecewise linear approximation to the ground truth. Our methodology is analyzed, evaluated and compared with exact solution and near-perfect approximations of the underlying relationships among variables achieving orders of magnitude improvement in analytics processing." @default.
• W2904194741 created "2018-12-22" @default.
• W2904194741 creator A5001331936 @default.
• W2904194741 creator A5052475427 @default.
• W2904194741 date "2018-12-27" @default.
• W2904194741 modified "2023-10-12" @default.
• W2904194741 title "Large-scale predictive modeling and analytics through regression queries in data management systems" @default.
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• W2904194741 doi "https://doi.org/10.1007/s41060-018-0163-5" @default.
• W2904194741 hasPublicationYear "2018" @default.
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