Lecture

Efficient Graph Queries: Memory Constraints

Description

This lecture discusses the challenges of scaling up pattern matching in large graphs, focusing on parallelizing pattern matching tasks and work-sharing strategies to improve scalability. It covers topics such as optimizing response time with work-sharing, parallelizing challenges, and exploiting dependencies for efficient pattern computation. The instructor presents a case study using datasets like Social Network Benchmark and Music Brainz, demonstrating the benefits of work-sharing over traditional methods. Additionally, the lecture explores context-aware pattern matching parallelization and partitioning techniques to reduce duplicate work. The goal is to efficiently execute graph queries over relational data under memory constraints, leveraging caching mechanisms and batch processing to minimize query execution time.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.