Hadoop Big data Projects final year ieee projects 2016-2017
Big data Projects concerned in collecting info from the numerous sources and these knowledge are interlinked to form world data. The huge knowledge applications are widely utilized in e-commerce, media, retail banking etc.
Big data characteristics are Dandelion, diverse, self governing. So the extracting of knowledge from bid information application isn’t an straightforward job.
The massive knowledge application needs some innovative techniques to improve the processing of large scale data within an endurable transpire time.
Many researchers are developing multiple applications for big information, like cloud computing framework, distributed parallel computing, distributed databases, etc. most of the leading IT based mostly firms are spent a lot of cash to manage their information in huge information atmosphere.
It can create an appetite for data discovery application. Developing countries or developed countries are taking a deep discussion about why we are using big data application for address the most important problems are faces by the govt.
IEEE BIG DATA PROJECTS :
- A Big Data Clustering Algorithm for Mitigating the Risk of Customer Churn
- A Parallel Patient Treatment Time Prediction Algorithm and Its Applications in Hospital Queuing-Recommendation in a Big Data Environment
- Adaptive Replication Management in HDFS based on Supervised Learning
- CaCo: An Efficient Cauchy Coding Approach for Cloud Storage Systems
- Clustering of Electricity Consumption Behavior Dynamics toward Big Data Applications
- Distributed In-Memory Processing of All k Nearest Neighbor Queries
- Dynamic Job Ordering and Slot Configurations for MapReduce Workloads
- Dynamic Resource Allocation for MapReduce with Partitioning Skew
- FiDoop-DP: Data Partitioning in Frequent Itemset Mining on Hadoop Clusters
- H2Hadoop: Improving Hadoop Performance using the Metadata of Related Jobs
- Hadoop Performance Modeling for Job Estimation and Resource Provisioning
- K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis
- Novel Scheduling Algorithms for Efficient Deployment of MapReduce Applications in Heterogeneous Computing Environments
- On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
- Optimization for Speculative Execution in Big Data Processing Clusters
- Processing Cassandra Datasets with Hadoop-Streaming Based Approaches
- Protection of Big Data Privacy
- RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop’s Configuration
- Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations
- Wide Area Analytics for Geographically Distributed Datacenters