High-performance data management and mining are well-known resource- and time-consuming activities that have attracted a great deal of interest from the research community. High-performance data manage- ment can be reasonably intended as a intermediate step of high-performance data mining activities over large- scale amounts of data , while still keeping unaltered the primary and self-contained focus of achieving effectiveness and efficiency in these task themselves. These topics are now of very-high interest, due to the emerging trends falling under the terms “ Big Data ” and “ Cloud Infrastructures ”. There exists a wide range of application scenarios where high-performance data management and mining play a critical role. Among these, we recall: prediction of natural disasters, anal- ysis of massive sensor and stream data, scientific computing and e-science, fraud detection, business intelligence, cloud intelligence, and so forth.

Models and Algorithms for High-Performance Data Management and Mining on Computational Grids and Clouds

CUZZOCREA, Alfredo Massimiliano
2014-01-01

Abstract

High-performance data management and mining are well-known resource- and time-consuming activities that have attracted a great deal of interest from the research community. High-performance data manage- ment can be reasonably intended as a intermediate step of high-performance data mining activities over large- scale amounts of data , while still keeping unaltered the primary and self-contained focus of achieving effectiveness and efficiency in these task themselves. These topics are now of very-high interest, due to the emerging trends falling under the terms “ Big Data ” and “ Cloud Infrastructures ”. There exists a wide range of application scenarios where high-performance data management and mining play a critical role. Among these, we recall: prediction of natural disasters, anal- ysis of massive sensor and stream data, scientific computing and e-science, fraud detection, business intelligence, cloud intelligence, and so forth.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2853866
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