HADOOP

Sai Info solution provide the Project Development & Training. We Develop Project for BE/ME/PHD. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. This brief tutorial provides a quick introduction to Big Data, MapReduce algorithm, and Hadoop Distributed File System.Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage. Due to the advent of new technologies, devices, and communication means like social networking sites, the amount of data produced by mankind is growing rapidly every year. The amount of data produced by us from the beginning of time till 2003 was 5 billion gigabytes. If you pile up the data in the form of disks it may fill an entire football field. The same amount was created in every two days in 2011, and in every ten minutes in 2013. This rate is still growing enormously. Though all this information produced is meaningful and can be useful when processed, it is being neglected.

What is Big Data?

Big data means really a big data, it is a collection of large datasets that cannot beprocessed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject,which involves various tools, technqiues and frameworks.

EXAMPLE

3-D Face Image identification from video streaming using Map Reduce

 ABSTRACT

massive face dentification system recognize a face from a lot of  faces at public places. There are two of techniques are used to  hit the goal: one is the 3Dface identification technique and the  other is the Hadoop detection technique. Face identification  technique find a similar face by 3D face features form mass  face data. The Hadoop is a parallel processing structure; it can  boost the computation ability. From imulation outcome, it is  demonstrate that our algorithm is an efficient  and accurate method for huge face identification.

Fig. 1 Object extraction and matching with the resultant image. 

Fig. 2 Shows Massive face identification structure in 

Hadoop.

INTRODUCTION

 Face identification is important task for several applications  on human being life. There are some examine was published  and described it below. I detect faces by using a hierarchical  knowledge-based method. I use three level resolutions in  their algorithm. The coarse-to-fine strategy reduce the computation is an advantage in this method. I also use local feature detector and random graph matching techniques to create a probabilistic method can locate a face  in a scene. By using five features like two eyes, two nostril,  and Nose/lip junction to depict a typical face. I am trying to define a facial template and relative distances of any pair  official features. This method can detect the testing object is  a human face or not. I will use a general and complete face detection technique. It  is a valuable method for face detection and surveying  detecting faces in images. For face image comparison, it is a hard work because it  needs a lot of working out and it cannot achieve 100%  accuracy. If I want to improve the comparison accuracy then  the multiple face indifferent angle can be  achieve the goal.  Furthermore ,if I want search a people from public places, it  is more difficult because it is related to real time operation  problems. It is a massive computation. However, the parallel processing technique increases computational capacity. For  a huge data, it needs massive computation ability. It needs  several computer works together to share the data. Therefore, Hadoop structure is a suitable system for solving the huge face identification difficulty.

What is Map Reduce?

MapReduce is a processing technique and a program model for distributed computing based on java. The MapReduce algorithm contains two important tasks, namely Map and Reduce. Map takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key/value pairs). Secondly, reduce task, which takes the output from a map as an input and combines those data tuples into a smaller set of tuples. As the sequence of the name MapReduce implies, the reduce task is always performed after the map job.

The major advantage of MapReduce is that it is easy to scale data processing over multiple computing nodes. Under the MapReduce model, the data processing primitives are called mappers and reducers. Decomposing a data processing application into mappers and reducers is sometimes nontrivial. But, once we write an application in the MapReduce form, scaling the application to run over hundreds, thousands, or even tens of thousands of machines in a cluster is merely a configuration change. This simple scalability is what has attracted many programmers to use the MapReduce model.

If anyone is interested for doing Research in above subject for BTech/MTech/PHD Engineering project work

Kindly Contact Below

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Santosh Gore Sir

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Email: sai.info2009@gmail.com