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• W2183669956 abstract "There is a need to integrate SQL processing with more advanced machine learning (ML) analytics to drive actionable insights from large volumes of data. As a first step towards this integration, we study how to efficiently connect big SQL systems (either MPP databases or new-generation SQL-on-Hadoop systems) with distributed big ML systems. We identify two important challenges to address in the integrated data analytics pipeline: data transformation, how to efficiently transform SQL data into a form suitable for ML, and data transfer, how to efficiently handover SQL data to ML systems. For the data transformation problem, we propose an In-SQL approach to incorporate common data transformations for ML inside SQL systems through extended user-defined functions (UDFs), by exploiting the massive parallelism of the big SQL systems. We propose and study a general method for transferring data between big SQL and big ML systems in a parallel streaming fashion. Furthermore, we explore caching intermediate or final results of data transformation to improve the performance. Our techniques are generic: they apply to any big SQL system that supports UDFs and any big ML system that uses Hadoop InputFormats to ingest input data." @default.
• W2183669956 created "2016-06-24" @default.
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• W2183669956 date "2015-01-01" @default.
• W2183669956 modified "2023-10-09" @default.
• W2183669956 title "A Generic Solution to Integrate SQL and Analytics for Big Data" @default.
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• W2183669956 doi "https://doi.org/10.5441/002/edbt.2015.67" @default.
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