Judging by the increasing impact of machine learning on large-scale data analysis in the
last decade, one can anticipate a substantial growth in diversity of the machine learning …

Hadoop has emerged as a successful framework for large-scale data-intensive computing
applications. However, there is no research on performance models for the Hadoop …

MapReduce is a programming model to process a massive amount of data on cloud
computing. MapReduce processes data in two phases and needs to transfer intermediate …

Judging by the increasing impact of machine learning on large-scale data analysis in the
last decade, one can anticipate a substantial growth in diversity of the machine learning …

Processing large volumes of scientific data requires an efficient and scalable parallel
computing framework to obtain meaningful information quickly. In this paper, we evaluate a …

One of the most published programming models of cloud computing is MapReduce. Google
MapReduce and Apache Hadoop MapReduce are various implementations of the model …

Geoinformation technologies require fast processing of high and quickly increasing volumes
of all types of spatial data. Parallel computational approach and distributed systems …

Processing large volumes of scientific data requires an efficient and scalable parallel
computing framework to obtain meaningful information quickly. MapReduce is a …

This paper presents and discusses the MapReduce programming model (MapReduce) and
its implementations in functional programming languages such as Haskell, Cat, and F# …

Whenever a person or an automated system has to reason in uncertain domains, probability
theory is necessary. Probabilistic graphical models allow us to build statistical models that …