​​​DHS Informatics providing latest 2018-2019 IEEE projects on BigData/ HAndoop for the final year engineering students. DHS Informatics trains all students to develop their project with good idea what they need to submit in college to get good marks. DHS Informatics offers placement training in Bangalore and the program name is OJT – On Job Training, job seekers as well as final year college students can join in this placement training program and job opportunities in their dream IT companies. We are providing IEEE projects for B.E / B.TECH, M.TECH, MCA, BCA, DIPLOMA students from more than two decades.Type your paragraph here.

DHS Informatics provides a latest IEEE projects for final year CSE and ISE students on Big data Technology.        We are developing latest big data projects within the Hadoop ecosystem , Big data involves data storage data analysis becoming a new trend to store the data .


Find below our latest IEEE 2018-2019 Big Data with Hadoop project listType your paragraph here.

BIGDATA / HANDOOP

Responsive Table
IEEE BIG DATA (Hadoop) PROJECTS
P.CODE TITLES BASEPAPER SYNOPSIS LINKS
DHS_BIG_1801 IEEE 2018:Secure Identity-based Data Sharing and Profile Matching for Mobile Healthcare Social Networks in Cloud Computing BasePaper Synopsis Link
DHS_BIG_1801 IEEE 2018:Capacity-aware Key Partitioning Scheme for Heterogeneous Big Data Analytic Engines BasePaper Synopsis Link
DHS_BIG_1801 IEEE 2018:Privacy preserving Reverse k-Nearest Neighbor Queries BasePaper Synopsis Link
DHS_BIG_1801 IEEE 2018:Client Side Secure Image Deduplication Using DICE Protocol BasePaper Synopsis Link
DHS_BIG_1801 IEEE 2018:Ciphertext-Policy Attribute-Based Signcryption With Verifiable Outsourced Designcryption for Sharing Personal Health Records BasePaper Synopsis Link
DHS_BIG_1801 IEEE 2018:MR-Mafia: Parallel Subspace Clustering Algorithm Based on MapReduce For Large Multi-dimensional Datasets BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Efficient Processing of Skyline Queries Using MapReduce BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Secure Big Data Storage and Sharing Scheme for Cloud Tenants BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Efficient Recommendation of De-identification Policies using MapReduce BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Hierarchy-Cutting Model based Association Semantic for Analyzing Domain Topic on the Web BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Large-Scale Multi-Modality Attribute Reduction with Multi-Kernel Fuzzy Rough Sets BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Question Quality Analysis and Prediction in Community Question Answering Services with Coupled Mutual Reinforcement BasePaper Synopsis Link
DHS_BIG_1701 IEEE 2017:Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2016:On Traffic-Aware Partition and Aggregation in Map Reduce for Big Data Applications BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2016:The SP Theory of Intelligence: Distinctive Features and Advantages BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2016:A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2016:Protection of Big Data Privacy BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2016:Towards a Virtual Domain Based Authentication on MapReduce. BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2016:CMiner Opinion Extraction and Summarization for Chinese Microblogs BasePaper Synopsis Link
DHS_BIG_1601 IEEE 2015:Secure sensitive data sharing on a big data platform BasePaper Synopsis Link

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.

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.Type your paragraph here.

Latest

Latest IEEE Big DATA (Hadoop) Projects

More IEEE Big Data Project List 2016 : View | Download

DHS Informatics believes in students’ stratification, we first brief the students about the technologies and type of BigData projects and other domain projects. After complete concept explanation of the IEEE BigData projects, students are allowed to choose more than one IEEE BigData projects for functionality details. Even students can pick one project topic from BigData and another two from other domains like BigData, data mining, image process, information forensic, big data, BigData, BigData, data science, block chain etc. DHS Informatics is a pioneer institute in Bangalore / Bengaluru; we are supporting project works for other institute all over India. We are the leading final year project centre in Bangalore / Bengaluru and having office in five different main locations Jayanagar, Yelahanka, Vijayanagar, RT Nagar & Indiranagar.

We allow the ECE, CSE, ISE final year students to use the lab and assist them in project development work; even we encourage students to get their own idea to develop their final year projects for their college submission.

DHS Informatics first train students on project related topics then students are entering into practical sessions. We have well equipped lab set-up, experienced faculties those who are working in our client projects and friendly student coordinator to assist the students in their college project works.

We appreciated by students for our Latest IEEE projects & concepts on final year BigData projects for ECE, CSE, and ISE departments.

Latest IEEE 2018-2019 projects on BigData with real time concepts which are implemented using Java, MATLAB, and NS2 with innovative ideas. Final year students of computer BigData, computer science, information science, electronics and communication can contact our corporate office located at Jayanagar, Bangalore for BigData project details.Type your paragraph here.

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