SOFTWARE PROJECTS


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IEEE 2017 BIG DATA/HADOOP BASED PROJECTS

This section consists of projects related to Big Data 2017 IEEE project list. Big Data analysis has been a very hot active during past few years and holds the potential as yet largely untapped to allow decision makers to track development progress. Latest Big Data topics, Latest Big Data Concepts for Diploma, Latest Big Data Concepts for Engineering,2017 IEEE Projects on Big Data for final year engineering Computer Science & Engineering students (CSE) and Final year engineering projects on Big Data for information science and engineering (ISE) students,Java based 2017 IEEE Projects on Big Data projects for M.Tech, CSE, CNE (Computer Network engineer) and BE CSE, BE ISE students, Latest Big Data Concepts for M-Tech, Big Data project centers in Bangalore and mysore with high quality Training and development, Latest J2EE Projects with recent Technology.Here is a list of project ideas for Big Data concepts.

IEEE 2017-2018 bigdata (hadoop) project list on java based for MTech /BE / BTech / MCA / M.sc students in bangalore.

TEB001 FIDOOP: PARALLEL MINING OF FREQUENT ITEMSETS USING MAPREDUCE SYNOPSIS
TEB002 SENTIMENT ANALYSIS OF TOP COLLEGES USING TWITTER DATA SYNOPSIS
TEB003 ON TRAFFIC-AWARE PARTITION AND AGGREGATION IN MAPREDUCE FOR BIG DATA APPLICATIONS SYNOPSIS
TEB004 THE SP THEORY OF INTELLIGENCE: DISTINCTIVE FEATURES AND ADVANTAGE SYNOPSIS
TEB005 A PARALLEL PATIENT TREATMENT TIME PREDICTION ALGORITHM AND ITS APPLICATIONS IN HOSPITAL QUEUING-RECOMMENDATION IN A BIG DATA ENVIRONMENT SYNOPSIS
TEB006 PROTECTION OF BIG DATA PRIVACY SYNOPSIS
TEB007 TOWARDS A VIRTUAL DOMAIN BASED AUTHENTICATION ON MAPREDUCE SYNOPSIS
TEB008 C MINER: OPINION EXTRACTION AND SUMMARIZATION FOR CHINESE MICRO BLOGS SYNOPSIS



Technofist offers Big data hadoop based IEEE projects for Mtech and BE final year computer science branch students. Here at technofist we use Hadoop big data platform to work on Big data projects. It is a java based programming which runs on apache hadoop. We have technical team who are skilled enough to provide solution on latest IEEE related big data projects. Get analytics and hadoop based projects on big data for students using java as core programming language.
Get top quality and trending IEEE big data projects from here and do it by yourself. We are continuously adding more big data final year project ideas, so you could find new opportunities in Big Data Science. Take reference or would like to start your training from our or yours idea on Big data projects.
Students belonging to third year mini projects or final year projects can use these projects as mini-projects as well as mega-projects. If you have questions regarding these projects feel free to contct us. You may also ask for abstract of a project idea that you have or want to work on.The own projects idea for diploma and Engineering students can also be done here.

ABOUT HADOOP

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.

Hadoop framework includes following Modules:

  • Hadoop MapReduce
  • Hadoop Distributed File System (HDFS™)

MapReduce

Hadoop MapReduce is a software framework for easily writing applications which process big amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner.

The term MapReduce actually refers to the following two different tasks that Hadoop programs perform:

  • The Map Task: This is the first task, which takes input data and converts it into a set of data, where individual elements are broken down into tuples (key/value pairs).

  • The Reduce Task: This task takes the output from a map task as input and combines those data tuples into a smaller set of tuples. The reduce task is always performed after the map task.

Hadoop Distributed File System (HDFS)

Hadoop File System was developed using distributed file system design. It is run on commodity hardware. Unlike other distributed systems, HDFS is highly fault tolerant and designed using low-cost hardware.
HDFS holds very large amount of data and provides easier access. To store such huge data, the files are stored across multiple machines. These files are stored in redundant fashion to rescue the system from possible data losses in case of failure. HDFS also makes applications available to parallel processing.

Advantages of Hadoop

  • Hadoop framework allows the user to quickly write and test distributed systems. It is efficient, and it automatic distributes the data and work across the machines and in turn, utilizes the underlying parallelism of the CPU cores.
  • Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather Hadoop library itself has been designed to detect and handle failures at the application layer.
  • Servers can be added or removed from the cluster dynamically and Hadoop continues to operate without interruption.
  • Another big advantage of Hadoop is that apart from being open source, it is compatible on all the platforms since it is Java based.

Features of Hadoop

  • It is suitable for the distributed storage and processing.
  • Hadoop provides a command interface to interact with HDFS.
  • The built-in servers of namenode and datanode help users to easily check the status of cluster.
  • Streaming access to file system data.
  • HDFS provides file permissions and authentication.