Big Data Projects 2017

Big Data Hadoop IEEE Projects 2017 – 2018

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A Scalable Data Chunk Similarity based Compression Approach for Efficient Big Sensing Data Processing on Cloud

A Systematic Approach Toward Description and Classification of Cybercrime Incidents

Achieving Efficient and Privacy-Preserving Cross-Domain Big Data Deduplication in Cloud

aHDFS: An Erasure-Coded Data Archival System for Hadoop Clusters

Big Data Privacy in Biomedical Research

Cost-Aware Big Data Processing across Geo-distributed Datacenters

Disease Prediction by Machine Learning over Big Data from Healthcare Communities

DyScale: a MapReduce Job Scheduler for Heterogeneous Multicore Processors

Efficient Processing of Skyline Queries Using MapReduce

FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters

Hadoop MapReduce for Mobile Clouds

Mining Human Activity Patterns from Smart Home Big Data for Healthcare Applications

PPHOPCM: Privacy-preserving High-order Possibilistic  c-Means Algorithm for Big Data Clustering with Cloud Computing

Practical Privacy-Preserving MapReduce Based K-means Clustering over Large-scale Dataset

Public Interest Analysis Based on Implicit Feedback of IPTV Users

Ring: Real-Time Emerging Anomaly Monitoring System over Text Streams

Robust Big Data Analytics for Electricity Price Forecasting in the Smart Grid

Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems

Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters

Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations

HADOOP 2017 button

Contact: 09952649690 / Email: jpinfotechprojects@gmail.com