This document lists 20 big data project titles from 2018-19. The projects focus on topics like data compression, cybercrime classification, data deduplication in cloud computing, disease prediction using machine learning on healthcare data, privacy-preserving data clustering, anomaly detection in text streams, electricity price forecasting using big data analytics, and service rating prediction by analyzing mobile user location data.

1. BIGDATA 2018-19 PROJECT TITLES
SNO PROJECT TITLE
01 A Scalable Data Chunk Similarity based Compression
Approach for Efficient Big Sensing Data Processing on Cloud
02 A Systematic Approach Toward Description and Classification
of Cybercrime Incidents
03 Achieving Efficient and Privacy-Preserving Cross-Domain Big
Data Deduplication in Cloud
04 aHDFS: An Erasure-Coded Data Archival System for Hadoop
Clusters
05 Big Data Privacy in Biomedical Research
06 Cost-Aware Big Data Processing across Geo-distributed
Datacenters
07 Disease Prediction by Machine Learning over Big Data from
Healthcare Communities
08 DyScale: a MapReduce Job Scheduler for Heterogeneous
Multicore Processors
09 Efficient Processing of Skyline Queries Using MapReduce
10 FiDoop-DP: Data Partitioning in Frequent Itemset Mining on
Hadoop Clusters
11 Hadoop MapReduce for Mobile Clouds
12 Mining Human Activity Patterns from Smart Home Big Data for
Healthcare Applications
13 PPHOPCM: Privacy-preserving High-order Possibilistic c-
Means Algorithm for Big Data Clustering with Cloud Computing
14 Practical Privacy-Preserving MapReduce Based K-means
Clustering over Large-scale Dataset
15 Public Interest Analysis Based on Implicit Feedback of IPTV
Users
16 Ring: Real-Time Emerging Anomaly Monitoring System over
Text Streams
17 Robust Big Data Analytics for Electricity Price Forecasting in
the Smart Grid
18 Scalable Uncertainty-Aware Truth Discovery in Big Data Social
Sensing Applications for Cyber-Physical Systems
19 Self-Adjusting Slot Configurations for Homogeneous and
Heterogeneous Hadoop Clusters
20 Service Rating Prediction by Exploring Social Mobile Users’
Geographical Locations