ArTS Archivio della ricerca di Trieste
ArTS è il sistema di gestione integrata dei dati della ricerca adottato dall' Università degli Studi di Trieste
EnglishHigh-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.
| Titolo: | Models and Algorithms for High-Performance Data Management and Mining on Computational Grids and Clouds | |
| Autori: | ||
| Data di pubblicazione: | 2014 | |
| Rivista: | ||
| 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. | |
| Handle: | http://hdl.handle.net/11368/2853866 | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/s10723-014-9316-5 | |
| Appare nelle tipologie: | 1.1 Articolo in Rivista |