Authors: Mike Heddes, Igor Nunes, Tony Givargis, Alex Nicolau

Published on: February 25, 2024

Impact Score: 8.0

Arxiv code: Arxiv:2402.15953

Summary

• What is new: A novel sketching method that provides fast updates for accurately estimating the cardinality of complex multi-join queries.
• Why this is important: The challenge of efficiently incrementally processing streaming data under memory constraints, particularly for complex multi-join queries.
• What the research proposes: Introduction of a novel sketching method that combines the advantages of Count sketch and AMS sketch extension for multi-join queries, offering fast update times.
• Results: The new estimator is unbiased with the same error guarantees as the AMS-based method and significantly faster update times, achieving orders of magnitude faster estimates with equal or better accuracy.

Technical Details

Technological frameworks used: nan

Models used: AMS sketch, Count sketch

Data used: Streaming data

Potential Impact

Data analytics platforms, real-time monitoring and decision-making tool providers, database management systems, and companies involved in big data and IoT.

Want to implement this idea in a business?

We have generated a startup concept here: StreamSight.