Decision Sciences
8 years ago

Big Data Optimization Boosts Efficiency, Transforms Data Processing Landscape

HAL (Le Centre pour la Communication Scientifique Directe)
Optimization for big joins and recursive query evaluation using intersection and difference filters in MapReduce
Thuong‐Cang Phan

Paper Summary

Related papers

Theme background image
Computer Science
8 years ago

Faster Big Data Processing Unlocks New Possibilities for Cloud Computing

Cloud Computing
Data Stream Management
Network Coding
1 view
Theme background image
Computer Science
4 years ago

Faster database queries could revolutionize how businesses and governments operate.

Data Stream Management
Trajectory Analysis
Graph Matching
1 view
Theme background image
Computer Science
8 years ago

Distributed data processing just got a massive boost with bloomfilter-powered MapReduce

Content-Centric Networking
Cloud Computing
Concept Drift
3 views

The article discusses how to make processing large amounts of data more efficient using a programming model called MapReduce. The researchers focused on improving the way complex operations like joins are handled in MapReduce. They introduced new filters called intersection and difference filters to optimize the process. By using these filters, they were able to significantly reduce the number of jobs needed for operations like joins and recursive queries, leading to faster and more efficient data processing.