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Examples might be simulations or tools for large-scale data analysis, or image recognition.
There is greatly expanded coverage of data mining for large-scale data.
Ben's research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning.
Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing.
For large-scale data sets, whose labeling is often done by overworked students, the process becomes time-consuming and expensive.
Implementing SciDB, an open-source array-based database management system (DBMS), may be more optimal for large-scale data analysis.
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MapReduce is regarded as an adequate programming model for large-scale data-intensive applications.
Recently, the computational requirements for large-scale data-intensive analysis of scientific data have grown significantly.
Hadoop has emerged as a successful framework for large-scale data-intensive computing applications.
A conceptual framework of breaking down workloads for large-scale data-centric systems into data access patterns, computation patterns, and load arrival patterns.
The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications.
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