MapReduce is a software framework that allows certain kinds of parallelizable or distributable problems involving large data sets to be solved using computing clusters. This paper introduces our experience of grouping internet users by mining a huge volume of web access log of up to 500 gigabytes. The application is realized using hierarchical clustering algorithms with Map-Reduce, a parallel p...

In this paper we present parallel implementation of genetic algorithm using map/reduce programming paradigm. Hadoop implementation of map/reduce library is used for this purpose. We compare our implementation with implementation presented in [1]. These two implementations are compared in solving One Max (Bit counting) problem. The comparison criteria between implementations are fitness converge...

MapReduce is a famous model for data-intensive parallel computing in shared-nothing clusters. One of the main issues in MapReduce is the fact of depending its performance mainly on data distribution. MapReduce contains simple load balance technique based on FIFO job scheduler that serves the jobs in their submission order but unfortunately it is insufficient in real world cases as it missed man...

A rapid growth of data in recent time, Industries and academia required an intelligent data analysis tool that would be helpful to satisfy the need to analysis a huge amount of data. MapReduce framework is basically designed to compute data intensive applications to support effective decision making. Since its introduction, remarkable research efforts have been put to make it more familiar to t...

This term paper focuses on how the big data is analysed in a distributed environment through Hadoop Map Reduce. Big Data is same as “small data” but bigger in size. Thus, it is approached in different ways. Storage of Big Data requires analysing the characteristics of data. It can be processed by the employment of Hadoop Map Reduce. Map Reduce is a programming model working parallel for large c...

In this paper, we consider the recent iterative extensions of the Map-Reduce framework and we argue that they would greatly benefit from the research work that was conducted in the area of dataflow computing more than thirty years ago. In particular, we suggest that the tagged-dataflow model of computation can be used as the formal framework behind existing and future iterative generalizations ...

The move towards a “semantic web” is driving the need for efficient querying ability over large datasets consisting of statements about web resources. RDF is a set of standards for describing and modeling data and is the backbone of the semantic web technologies. RDF datasets can be very large, and often are subject to complex queries with the intent of extracting and infering otherwise unseen ...

In Text categorization techniques like Text classification or clustering, finding frequent item sets is an acquainted method in the current research trends. Even though finding frequent item sets using Apriori algorithm is a widespread method, later DHP, partitioning, sampling, DIC, Eclat, FP-growth, H-mine algorithms were shown better performance than Apriori in standalone systems. In real sce...

ARTICLE INFO Hadoop represents a Java-based distributed computing framework that is designed to support applications that are implemented via the MapReduce programming model. Hadoop performance however is significantly affected by the settings of the Hadoop configuration parameters. Unfortunately, manually tuning these parameters is very time-consuming. Existing system uses Random forest approa...

We propose a flexible framework that can be easily customized to enforce a large variety of information flow properties. Our framework combines the ideas of secure multi-execution and map-reduce computations. The information flow property of choice can be obtained by simply changes to a map (or reduce) program that control parallel executions. We present the architecture of the enforcement mech...