IEEE 2016 HADOOP BIGDATA PROJECTS

1 CPH1601 A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn
BIG DATA
2 CPH1602 A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment
BIG DATA
3 CPH1603 Adaptive Replication Management in HDFS based on Supervised Learning
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4 CPH1604 CaCo: An Efficient Cauchy Coding Approach for Cloud Storage Systems
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5 CPH1605 Clustering of Electricity Consumption Behavior Dynamics toward Big Data Applications
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6 CPH1606 Distributed In-Memory Processing of All k Nearest Neighbor Queries
BIG DATA
7 CPH1607 Dynamic Job Ordering and Slot Configurations for MapReduce Workloads
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8 CPH1608 Dynamic Resource Allocation for MapReduce with Partitioning Skew
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9 CPH1609 FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters
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10 CPH1610 H2Hadoop: Improving Hadoop Performance using the Metadata of Related Jobs
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11 CPH1611 Hadoop Performance Modeling for Job Estimation and Resource Provisioning
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12 CPH1612 K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis
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13 CPH1613 Novel Scheduling Algorithms for Efficient Deployment of MapReduce Applications in Heterogeneous Computing Environments
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14 CPH1614 On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
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15 CPH1615 Optimization for Speculative Execution in Big Data Processing Clusters
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16 CPH1616 Processing Cassandra Datasets with Hadoop-Streaming Based Approaches
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17 CPH1617 Protection of Big Data Privacy
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18 CPH1618 RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop’s Configuration
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19 CPH1619 Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations
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20 CPH1620 Wide Area Analytics for Geographically Distributed Datacenters
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