Sentence examples for managing large datasets from inspiring English sources
In the end, SDAV aims to deliver end-to-end solutions, from managing large datasets as they are being generated to creating new algorithms for analyzing the data on emerging architectures.
There is a need to manage such large volumes of data and to cluster them easily for data analytics while minimizing maximum inter-cluster distances and managing large datasets.
Our data-driven categories may be one useful approach, and computer-assisted coding and extraction of themes may be another way managing large datasets.
This is a commercial extension that offers a set of components for accessing Apache Hadoop Mapreduce framework (distributed storage and distributed processing on clusters) and Apache Hive (data warehouse software for querying and managing large datasets residing in distributed storage).
Jessica Hedge tells Julie Gould how she learned to code as PhD student, and the freedom and flexibility it provides to manage large datasets.
Existing clustering algorithms require scalable solutions to manage large datasets.
Hadoop may be used over Spark for the other following reasons: low-cost hardware, lower latency value, and the capacity to manage large datasets with the help of an HDFS.
We apply the Hadoop MapReduce standard K-means clustering algorithm to manage large datasets and introduce a new metric for similarity measurements such that the distances between objects exhibit high levels of intra-cluster similarity and low levels of inter-cluster similarity.
Yet, at the same time, it is able to manage large datasets and to overcome sequencing errors.
The tremendous increase in molecular data permits phylogenetic analyses of very long sequences and of many species, but also requires methods to help manage large datasets.
Administrative data sources allows to manage large datasets and, as a general rule, when sample sizes are sufficiently large for the central limit theorem to exert itself, simple methods should be preferred.
