Snowflake inc. (20240303373). AGGREGATION CONSTRAINTS IN A QUERY PROCESSING SYSTEM simplified abstract

AGGREGATION CONSTRAINTS IN A QUERY PROCESSING SYSTEM

Organization Name

snowflake inc.

Inventor(s)

Khalid Zaman Bijon of Santa Cruz CA (US)

Bowei Chen of San Bruno CA (US)

Thierry Cruanes of San Mateo CA (US)

Simon Holm Jensen of Menlo Park CA (US)

Allison Waingold Lee of Pebble Beach CA (US)

Valentin K. Kuznetsov of Bellevue WA (US)

Jun Li of Bothell WA (US)

Subramanian Muralidhar of Mercer Island WA (US)

Carl Yates Perry of Burlingame CA (US)

David Schultz of Piedmont CA (US)

Zixi Zhang of San Mateo CA (US)

AGGREGATION CONSTRAINTS IN A QUERY PROCESSING SYSTEM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240303373 titled 'AGGREGATION CONSTRAINTS IN A QUERY PROCESSING SYSTEM

The abstract of the patent application describes a cloud data platform that receives a query for a shared dataset, performs an operation on the data accessed from the dataset, enforces an aggregation constraint policy attached to a table in the dataset, and generates an output based on the enforced policy.

• The cloud data platform receives a query for a shared dataset and performs an operation on the data accessed from the dataset.
• An aggregation constraint policy attached to a table in the dataset restricts output of data values and is enforced based on the context of the query.
• The platform generates an output to the query by enforcing the aggregation constraint policy on the data accessed from the shared dataset.

Potential Applications: - Data analysis and processing in cloud-based systems - Database management and optimization - Data security and privacy enforcement in shared datasets

Problems Solved: - Ensuring data privacy and security in shared datasets - Optimizing data processing and analysis in cloud environments - Enforcing data aggregation constraints for specific queries

Benefits: - Improved data security and privacy protection - Enhanced data processing efficiency and optimization - Streamlined query execution and output generation

Commercial Applications: Title: Cloud Data Platform for Secure and Efficient Data Processing This technology can be utilized in cloud computing services, data analytics platforms, and database management systems to enhance data security, privacy enforcement, and query optimization. Market implications include improved data processing capabilities, increased efficiency in data analysis, and enhanced security measures for shared datasets.

Questions about the technology: 1. How does the aggregation constraint policy enhance data security in shared datasets? 2. What are the potential implications of enforcing aggregation constraints on query performance and output generation?

Original Abstract Submitted

the cloud data platform receives a first query directed towards a shared dataset, the first query identifying a first operation. the platform accesses a first set of data from the shared dataset to perform the first operation, the first set of data including data accessed from a first table of the shared dataset. the cloud data platform determines that an aggregation constraint policy is attached to the first table, the aggregation constraint policy restricts output of data values stored in the first table and enforces the aggregation constraint policy on the first query based on a context of the first query. the cloud data platform generates an output to the first query based on the first set of data and the first operation, based on enforcing the aggregation constraint policy on the first query.