Results 201 to 210 of about 10,108 (225)
Some of the next articles are maybe not open access.
2014
The computing power of modern high performance systems cannot be fully exploited using traditional parallel programming models. On the other hand, the growing demand for processing big data volumes requires a better control of the workflows, an efficient storage management, as well as a fault-tolerant runtime system. Trying to offer our proper solution
Tiberiu Rotaru +2 more
openaire +1 more source
The computing power of modern high performance systems cannot be fully exploited using traditional parallel programming models. On the other hand, the growing demand for processing big data volumes requires a better control of the workflows, an efficient storage management, as well as a fault-tolerant runtime system. Trying to offer our proper solution
Tiberiu Rotaru +2 more
openaire +1 more source
An Overview of the MapReduce Model
2017Data is getting accumulated fast in various domains all over the world and the data size varies from terabytes to yottabytes. Such huge size data are known as Big Data. Extraction of meaningful information from raw data using special patterns are called Data Mining and sophisticated algorithms have been designed for this purpose.
S. Rajeswari +3 more
openaire +1 more source
A MapReduce Algorithm for EL+. [PDF]
Recently, the use of the MapReduce framework for distributed RDF Schema reasoning has shown that it is possible to compute the deductive closure of sets of over a billion RDF triples within a reasonable time span [22], and that it is also possible to carry the approach over to OWL Horst [21].
Mutharaju, Raghava +2 more
openaire +1 more source
Distributed data management using MapReduce
ACM Computing Surveys, 2014Beng Chin Ooi, M Tamer Özsu, Sai Wu
exaly
The family of mapreduce and large-scale data processing systems
ACM Computing Surveys, 2013Sherif Sakr, Anna Liu
exaly
Classification Framework of MapReduce Scheduling Algorithms
ACM Computing Surveys, 2015Nidhi Tiwari +2 more
exaly

