In-memory parallel processing of massive remotely sensed data using an apache spark on hadoop yarn model
W Huang, L Meng, D Zhang… - IEEE Journal of Selected …, 2016 - ieeexplore.ieee.org
MapReduce has been widely used in Hadoop for parallel processing larger-scale data for
the last decade. However, remote-sensing (RS) algorithms based on the programming …
the last decade. However, remote-sensing (RS) algorithms based on the programming …
Optimization in the catalyst optimizer of Spark SQL
M Chawla, V Baniwal - Turkish Journal of Electrical …, 2018 - journals.tubitak.gov.tr
Apache Spark is one of the most technically challenged frameworks for cluster computing in
which data are processed in a parallel fashion. The cluster consists of unreliable machines …
which data are processed in a parallel fashion. The cluster consists of unreliable machines …
Finding the best Box-Cox transformation from massive datasets on Spark
In order to find the best linear regression model or polynomial regression model that fits the
data, traditional methods have to read the whole datasets repetitively and incur many …
data, traditional methods have to read the whole datasets repetitively and incur many …
Improving Big Data Box-Cox Transformation on Spark
H Fang - 2017 - search.proquest.com
This study investigates improving Spark computation with Box-Cox Information Array when it
is used to implement the linear regression models. In order to find the best linear regression …
is used to implement the linear regression models. In order to find the best linear regression …
AQuSerM 2006: Advances in Quality of Service Management
I Poernomo, G Wang - 2006 10th IEEE International Enterprise …, 2006 - computer.org
In order to find the best linear regression model or polynomial regression model that fits the
data, traditional methods have to read the whole datasets repetitively and incur many …
data, traditional methods have to read the whole datasets repetitively and incur many …