Research Paper Title: An overview of the Hadoop/MapReduce/HBase framework

and its current applications in bioinformatics

Author : Taylor, Ronald C

Published at:The 11th Annual Bioinformatics Open Source Conference

(BOSC) 2010 Boston, MA, USA. 9-10 July 2010

Academic Journal, BMC Bioinformatics.DOI: 10.1186/1471-2105-11-S12-S1.

Academic Search Topic Chosen by:

Qing SunHarini Chilamantula

Sivagami Nachiyappan

04/21/23

Background Information

Nowadays, data centers are consuming a lot of energy for big data to

store and to maintain, but not in an efficient fashion.

There are several types of waste at different levels

o space for keeping large servers (data centers),

o effort for maintain,

o infrastructure,

o machine,

o system level waste (resource waste) .

About Hadoop

Hadoop Map/Reduce is a software framework for easily writing applications which process vast amount of data in parallel on large clusters of commodity hardware.

Hadoop is a large scale distributed file system modeled after the Google File System.

The key feature of Hadoop is fault- tolerance to the hardware failures.

By using Hadoop, we can run terabytes of data and applications on thousands of nodes in the network.

Hadoop implements MapReduce, a programming model, using the Hadoop Distributed File System (HDFS).

MapReduce in Big Data Analysis

MapReduce is used to divide the large applications into small blocks and distribute them to the other nodes in the network.

Master node will collect all the solutions back.

Benefits of Hadoop

Easily process vast amount of data in parallel on large clusters

It provides more scalability

Volume – Terabytes, petabytes and beyond.

Velocity – Speed access, Real-Time Data Analytics.

Variety – Centralized (Data moves to Analytics), Distributed (Analytics

moves to Data).

Value – Graph Algorithm, predictive Machine Learning, Commodity

Hardware.

Hadoop is already a key to delivering

on the promise of bioinformatics.

The Hadoop is also in the process of

providing a platform in which it is

easy to analyze and integrate the

various large, disparate data sources

into one data warehouse.

In near feature Hadoop hold some

more incridible contributions,

regarding store and process complex

data

It would have made easy to

understand if they have any

Pictorial representations.

When you have OLTP needs.

MR is not suitable for a large

number of short on-line

transactions .

Using MR is time consuming.

Conclusion Suggestions

Term Project Proposal

Title: APP’s Search Application

Team Members: Qing Sun

Harini Chilamantula Sivagami Nachiyappan

Faculty Advisor – Dr. Meiliu LuCSC Department – Fall 2013

California State University, Sacramento

Project Motivations

This search application is designed for iphone, Android, Windows, Tizen, VBM, Meego and ipad apps

It includes utility applications, performance applications, gamming applications.

It is a Search- based application for an app.

It is the faster and easier way to search different Operating System supportable apps at one common place.

Goals of Project

This application can display the requirements of a user required app and can also connect user to appropriate web page to download the app from the app store.

By using app search application user can know the features, details, how to use and availability of particular apps.

How we reach our goals for a successful project

Design an Online Transactional Processing (OLTP) application to get details of various applications from their respective websites and display the result of the query in a webpage.

Design an Online Analytical Processing (OLAP) to integrate the data from various data sources, create our own data mart and display the results of the customer’s query on a webpage.

Extract data from various data sources, transform the data and present the data.

Project Schedule

WEEK OBJECTIVES

10 Project Proposal

11 Group Task Assignments / Data collection

12 Progress Peport / Design Task

13 Create Presentation Slides / Build initial website

14 Presentation Practice / Project Presentation

15 Prepare Final Written Repoet

References:

http://spectrum.ieee.org/automaton/robotics/robotics-software/cloud-robotics

https://developers.facebook.com/docs/guides/appcenter/

http://apphelp.copilotlive.com/copilot/en-US/?platform=android

http://copilotlive.com/us/personal/android.asp

http://copilotlive.com/us/store/android.asp

http://www.biomedcentral.com/1471-2105/11/S12/S1

http://link.springer.com/article/10.1186%2F1471-2105-11-S12-S1

http://www.roadsideamerica.com/mobile/roadside/ios/

http://www.roadsideamerica.com/mobile/roadside/ios/faq

http://stackoverflow.com/questions/18585839/what-are-the-disadvantages-of-mapreduce