HADOOP OPEN SOURCE PROJECT
HADOOP OPEN SOURCE PROJECT best gift for computer engineers,we can access the huge level of data very easily.
HADOOP OPEN SOURCE PROJECT:
- BOLAS: Bipartite-Graph Oriented Locality-Aware Scheduling for MapReduce Tasks
- Increasing performance of parallel and distributed systems in high performance computing using weight based approach
- An efficient unstructured big data analysis method for enhancing performance using machine learning algorithm
- Selection of Virtual Machines Based on Classification of MapReduce Jobs
- FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce
- Valence arousal similarity based recommendation services
- A novel approach for efficient handling of small files in HDFS
- Multithreaded implementation of data intensive applications with overlapped I/O and computation
- Image filtering with MapReduce in pseudo-distribution mode
- Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data
- Frequent itemset mining for Big Data in social media using ClustBigFIM algorithm
- Exploiting Analytics Shipping with Virtualized MapReduce on HPC Backend Storage Servers
- Challenges of Cloud Computing & Big Data Analytics HADOOP OPEN SOURCE PROJECT
- HM: A Column-Oriented MapReduce System on Hybrid Storage
- Analysis of MapReduce scheduling and its improvements in cloud environment
- Handling Big Data Efficiently by Using Map Reduce Technique HADOOP OPEN SOURCE PROJECT
- Virtual Shuffling for Efficient Data Movement in MapReduce
- Solutions for Processing K Nearest Neighbor Joins for Massive Data on MapReduce
- Study on emerging implementations of MapReduce
- MRDataCube: Data cube computation using MapReduce HADOOP OPEN SOURCE PROJECT
- An approach for pre-filtering images from big data sets
- Optimizing OLAP Cubes Construction by Improving Data Placement on Multi-nodes Clusters
- Groupwise analytics via adaptive MapReduce
- DualTable: A hybrid storage model for update optimization in Hive
- Blind men and an elephant coalescing open-source, academic, and industrial perspectives on BigData
