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Topic Chosen by: Q ing Sun Harini Chilamantula Sivagami Nachiyappan

Topic Chosen by: Q ing Sun Harini Chilamantula Sivagami Nachiyappan.

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Topic Chosen by: Q ing Sun Harini Chilamantula Sivagami Nachiyappan

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  1. Topic Chosen by: Qing Sun Harini Chilamantula Sivagami Nachiyappan Research Paper Title: An overview of the Hadoop/MapReduce/HBaseframework and its current applications inbioinformaticsAuthor : Taylor, Ronald CPublished at:The 11th Annual Bioinformatics Open Source Conference (BOSC) 2010 Boston, MA, USA. 9-10 July 2010Academic Journal, BMC Bioinformatics.DOI: 10.1186/1471-2105-11-S12-S1. Academic Search

  2. 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 • space for keeping large servers (data centers), • effort for maintain, • infrastructure, • machine, • system level waste (resource waste) .

  3. About Hadoop • Hadoop Map/Reduce is a software framework for easily writing applications which process vastamount of datain 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).

  4. 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.

  5. 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.

  6. Conclusion Suggestions • 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.

  7. Term Project Proposal • Title: APP’s Search Application • Team Members: Qing Sun Harini Chilamantula SivagamiNachiyappan Faculty Advisor – Dr. Meiliu Lu CSC Department –Fall 2013 California State University, Sacramento

  8. 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.

  9. 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.

  10. 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.

  11. Project Schedule

  12. 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

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