In this paper, we discuss a Grid data mining system based on the MapReduce paradigm of computing. The MapReduce paradigm emphasizes system automation of fault tolerance and redundancy, while keeping the programming model for the user very simple. MapReduce is built closely on top of a distributed file system, that allows efficient distributed storage of large data sets, and allows computation t...

In recent years the MapReduce programming model has been widely used for developing parallel data-intensive applications. As a result of its popularity, there exist many implementations of the MapReduce model on different parallel architectures including on massively parallel programmable GPUs. A basic challenge in implementing a MapReduce runtime system is the wide diversity of applications de...

MapReduce is a programming model for parallel data processing widely used in Cloud computing environments. Current MapReduce implementations are based on centralized master-slave architectures that do not cope well with dynamic Cloud infrastructures, like a Cloud of clouds, in which nodes may join and leave the network at high rates. We have designed an adaptive MapReduce framework, called P2P-...

MapReduce programs and systems are large-scale, highly distributed and parallel, consisting of many interdependent Map and Reduce tasks executing simultaneously on potentially large numbers of cluster nodes. They typically process large datasets and run for long durations. Thus, diagnosing failures in MapReduce programs is challenging due to their scale. This renders traditional time-based Serv...

MapReduce is a programming model used by Google to process large amount of data in a distributed computing environment. It is usually used to perform distributed computing on clusters of computers. Computational processing of data stored on either a file system or a database usually occurs. MapReduce takes the advantage of locality of data, processing data on or near the storage areas, thereby ...

Scale of data generated and processed is exploding in the Big Data era. The MapReduce system popularized by open-source Hadoop is a powerful tool for the exploding data problem, and is widely employed in many areas involving large scale of data. In many circumstances, hypothetical MapReduce systems must be evaluated, e.g. to provision a new MapReduce system to provide certain performance goal, ...

Information Retrieval (IR) systems require input corpora to be indexed. The advent of terabyte-scale Web corpora has reinvigorated the need for efficient indexing. In this work, we investigate distributed indexing paradigms, in particular within the auspices of the MapReduce programming framework. In particular, we describe two indexing approaches based on the original MapReduce paper, and comp...

Distributed computing frameworks are the fundamental component of distributed systems. They provide an essential way to support efficient processing big data on clusters or cloud. The size increases at a pace that is faster than increase in capacity clusters. Thus, based MapReduce model not adequate analysis tasks which often require running complex analytical algorithms extremely sets terabyte...

There is a growing trend of applications that should handle big data. However, analyzing big data is a very challenging problem today. For such applications, the MapReduce framework has recently attracted a lot of attention. Google’s MapReduce or its open-source equivalent Hadoop is a powerful tool for building such applications. In this tutorial, we will introduce the MapReduce framework based...