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]. Following this lead, in this paper we provide a MapReduce algorithm for the description logic EL+, more...

Clustering is the process of grouping similar data into clusters and dissimilar data into different clusters. Density-based clustering is a useful clustering approach such as DBSCAN and OPTICS. The increasing volume of data and varying size of data sets lead the clustering process challenging. So that we propose a parallel framework of clustering with advanced approach called MapReduce. We deve...

Over the past decade several frameworks such as Google MapReduce have been developed that allow data processing with unprecedented scale due to their high scalability and fault tolerance. However, these systems provide both new and existing challenges for workload balancing that have not yet been fully explored. The MapReduce model in particular has some inherent limitations when it comes to wo...

The MapReduce pattern can be found in many important applications, and can be exploited to significantly improve system parallelism. Unlike previous work, in which designers explicitly specify how to exploit the pattern, we develop a compilation approach for mapping applications with the MapReduce pattern automatically onto Field-Programmable Gate Array (FPGA) based parallel computing platforms...

MapReduce simplifies parallel programming, abstracting the programmer responsibilities as synchronization and task management. The paradigm allows the programmer to write sequential code which is automatically parallelized. The MapReduce frameworks developed today are designed for situations where all keys generated by the Map phase must fit into main memory. However certain types of workload h...

In this study, we compare three approaches to implement graph algorithms SQL, MapReduce, and GraphChi in terms of performance and cost efficiency. Since MapReduce was introduced, it has been considered as a standard tool to analyze large scale data including graph data. However, we believe that there is a ‘sweet spot’ in which RDBMS can outperform MapReduce. We think that the performance of the...