MapReduce [10] gives us an appropriate model for distributed parallel computing. There are several features which are proved useful: 1) centralized job distribution. 2) Fault tolerance mechanism for both masters and workers. Although there is controversies about MapReduce capability to replace standard RDBMS [12, 13], it is reasonable that existing proposals to use MapReduce in relational data ...

Application-level interoperability is defined as the ability of an application to utilize multiple distributed heterogeneous resources. Such interoperability is becoming increasingly important with increasing volumes of data, multiple sources of data as well as resource types. The primary aim of this paper is to understand different ways and levels in which application-level interoperability ca...

MapReduce is a powerful distributed data processing model that is currently adopted in a wide range of domains to efficiently handle large volumes of data, i.e., cope with the big data surge. In this paper, we propose an approach to formal derivation of the MapReduce framework. Our approach relies on stepwise refinement in Event-B and, in particular, the event refinement structure approach – a ...

Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing. Along with these new users, important new workloads have emerged which feature many small, short, and increasingly interactive jobs in addition to the large, long-running batch jobs for which MapReduce was originally designed. As interactive, large-scale query pro...

Load balance is important for MapReduce to reduce job duration, increase parallel efficiency, etc. Previous work focuses on coarse-grained scheduling. This study concerns finegrained scheduling on MapReduce operations. Each operation represents one invocation of the Map or Reduce function. Scheduling MapReduce operations is difficult due to highly skewed operation loads, no support to collect w...

Distributed data mining (DDM) which often utilizes autonomous agents is a process to extract globally interesting associations, classifiers, clusters, and other patterns from distributed data. As datasets double in size every year, moving the data repeatedly to distant CPUs brings about high communication cost. In this paper, data cloud is utilized to implement DDM in order to move the data rat...

Skyline queries are useful for finding interesting tuples from a large data set according to multiple criteria. The sizes of data sets are constantly increasing and the architecture of back-ends are switching from single-node environments to non-conventional paradigms like MapReduce. Despite the usefulness of skyline queries, existing works on skyline computation in MapReduce do not take full a...

MapReduce is a popular programming model for distributed data processing and Big Data applications running on clouds. Extensive research has been conducted either to improve the dependability or to increase performance of MapReduce, ranging from adaptive and on-demand fault-tolerance solutions, adaptive task scheduling techniques to optimized job execution mechanisms. This paper investigates an...

Data analytics is becoming increasingly prominent in a variety of application areas ranging from extracting business intelligence to processing data from scientific studies. MapReduce programming paradigm lends itself well to these data-intensive analytics jobs, given its ability to scale-out and leverage several machines to parallely process data. In this work we argue that such MapReduce-base...

MapReduce is an efficient distributed computing model on large data sets. The data processing is fully distributed on huge amount of nodes, and a MapReduce cluster is of highly scalable. However, single-node performance is gradually to be a bottleneck in computeintensive jobs, which makes it difficult to extend the MapReduce model to wider application fields such as largescale image processing ...