Sentence examples for managing large sets from inspiring English sources

There is certainly unemployment in Silicon Valley, including in tech, especially for engineers without experience in the programming languages in vogue and skills like managing large sets of data.

In this paper we address the problem of scalability in temporal reasoning by providing a collection of new algorithms for efficiently managing large sets of qualitative temporal relations.

Hadoop is made up of these various projects to help users with the tasks they need to undertake when managing large sets of data, such as Hive, a data warehouse for Hadoop, and HBase, a scalable, distributed database — both of which are supported in AWS.

Without relevance, our ability to manage large sets of information is inefficient.

DreamQuark (France): DreamQuark is working on various solutions to manage large sets of structured and unstructured data.

Both cloud and grid are computing paradigms that manage large sets of distributed resources, and the scientific community would benefit from their convergence.

We describe a user-friendly information system able to manage large sets of sequencing data.

It is therefore useful where multiple researchers are working on a project, particularly in multi-disciplinary research teams were not all members have experience of qualitative data analysis, and for managing large data sets where obtaining a holistic, descriptive overview of the entire data set is desirable.

No single program running on a 32-bit processor can, without a lot of fussing, use more than 4GB of memory, which can be a problem when managing large data sets or handling extremely complex graphics.

MapReduce framework is widely used for processing and managing large data sets in a distributed cluster, which has been used for numerous applications such as, document clustering, access log analysis, generating search indexes and various other data analytical operations.

Mapping MapReduce frameworks to Cloud Architecture became a must in last years because of the need of managing large data sets and Big Data in fast, reliable (and as cheap as possible) way.