BIGDATA PROJECTS


Big Data is having a massive growth in application industry as well as in growth of Real time applications and technologies, Big Data can be used with automatic and semiautomatic in a lot of ways such as for huge data with the Encryption and decryption Techniques as well as executing the commands.
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IEEE 2017-18 BIG DATA/HADOOP BASED PROJECTS

we present real time IEEE Big Data projects for computer science and information science engineering students with high-quality Explanation and guidance and implementation. This section consists of projects related to Big Data 2018 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

IEEE 2017-18 bigdata (hadoop) project list on java based for MTech /BE / BTech / MCA / M.sc students in bangalore.

TEB001 SOCIALQ&A: AN ONLINE SOCIAL NETWORK BASED QUESTION AND ANSWER SYSTEM ABSTRACT
TEB002 PRIVACY-PRESERVING DATA ENCRYPTION STRATEGY FOR BIG DATA IN MOBILE CLOUD COMPUTING ABSTRACT
TEB003 NETSPAM: A NETWORK-BASED SPAM DETECTION FRAMEWORK FOR REVIEWS IN ONLINE SOCIAL MEDIA ABSTRACT
TEB004 EFFICIENT PROCESSING OF SKYLINE QUERIES USING MAPREDUCE ABSTRACT
TEB005 FIDOOP-DP: DATA PARTITIONING IN FREQUENT ITEMSET MINING ON HADOOP CLUSTERS ABSTRACT
TEB006 RESEARCH DIRECTIONS FOR ENGINEERING BIG DATA ABSTRACT
TEB007 SCALABLE DATA CHUNK SIMILARITY BASED COMPRESSION APPROACH FOR EFFICIENT BIG SENSING DATA PROCESSING ABSTRACT
TEB008 USER-CENTRIC SIMILARITY SEARCH ABSTRACT
TEB009 SECURE BIG DATA STORAGE AND SHARING SCHEME FOR CLOUD TENANTS ABSTRACT
TEB010 EFFICIENT RECOMMENDATION OF DE-IDENTIFICATION POLICIES USING MAPREDUCE ABSTRACT
TEB011 HIERARCHY-CUTTING MODEL BASED ASSOCIATION SEMANTIC FOR ANALYZING DOMAIN TOPIC ON THE WEB ABSTRACT
TEB012 LARGE-SCALE MULTI-MODALITY ATTRIBUTE REDUCTION WITH MULTI-KERNEL FUZZY ROUGH SETS ABSTRACT
TEB013 A SECURE AND VERIFIABLE ACCESS CONTROL SCHEME FOR BIG DATA STORAGE IN CLOUDS ABSTRACT
TEB014 QUESTION QUALITY ANALYSIS AND PREDICTION IN COMMUNITY QUESTION ANSWERING SERVICES WITH COUPLED MUTUAL REINFORCEMENT ABSTRACT
TEB015 FIDOOP: PARALLEL MINING OF FREQUENT ITEMSETS USING MAPREDUCE ABSTRACT
TEB016 SENTIMENT ANALYSIS OF TOP COLLEGES USING TWITTER DATA ABSTRACT
TEB017 THE SP THEORY OF INTELLIGENCE: DISTINCTIVE FEATURES AND ADVANTAGE ABSTRACT
TEB018 ON TRAFFIC-AWARE PARTITION AND AGGREGATION IN MAPREDUCE FOR BIG DATA APPLICATIONS ABSTRACT
TEB019 A PARALLEL PATIENT TREATMENT TIME PREDICTION ALGORITHM AND ITS APPLICATIONS IN HOSPITAL QUEUING-RECOMMENDATION IN A BIG DATA ENVIRONMENT ABSTRACT
TEB020 PROTECTION OF BIG DATA PRIVACY ABSTRACT
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We provide basic classes on java, Big data and Hadoop, HDFS Concepts are also explained on Big Data with good examples and latest ideas. We also provide abstract and complete explanation on synopsis, Latest IEEE projects are available on Big Data, titles and abstracts can be downloaded from our website. best project institute in Bangalore for carrying out final year projects on Big Data Domain. 8th sem computer science engineering students and information science students can call our head office situated in R.T.Nagar for Big Data project explanation. Genuine lab set up is obtainable for students to practice codes and implementation. A project lead will be fixed to each batch of cse and ise students to carry out final year engineering projects on Big Data. Big Data domain based projects are most brilliant to choose the career after engineering M-Tech. BE students are requested to inscribe a mail or contact our branches at the first, soon after exams and complete the projects in vacations to keep away from last minute rush.

Technofist With wonderful lab set up, Infrastructure and with full information faculties are available around the clock to guide final year computer science and information science students to complete Big data or hadoop projects on time. Technofist is one of the most outstanding institute for Big Data 2017 IEEE project implementation. Several of Big Data projects with IEEE papers are available with us. We provide Online support using teamviewer , skype will be accessible for out station students as well as out of country students.

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.

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