Snowflake inc. (20240289333). METADATA SEARCH VIA N-GRAM INDEX simplified abstract

METADATA SEARCH VIA N-GRAM INDEX

Organization Name

snowflake inc.

Inventor(s)

Lin Chan of Bellevue WA (US)

Tianyi Chen of Kirkland WA (US)

Benoit Dageville of San Mateo CA (US)

Yiming Kang of Seattle WA (US)

Jun Luo of Bellevue WA (US)

Nithin Mahesh of Redmond WA (US)

Eric Robinson of Sammamish WA (US)

Brian Smith of Hillsborough CA (US)

METADATA SEARCH VIA N-GRAM INDEX - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240289333 titled 'METADATA SEARCH VIA N-GRAM INDEX

The abstract describes a patent application for creating an n-gram index to conduct faster searches, including partial n-gram components and recent log data analysis.

• Simplified Explanation:

An n-gram index is created for faster searches, including partial n-gram components and recent log data analysis.

• Key Features and Innovation:

- Creation of an n-gram index for faster search results - Inclusion of partial n-gram components for more relevant data - Utilization of recent log data for search optimization

• Potential Applications:

- Information retrieval systems - Search engines - Data analysis tools

• Problems Solved:

- Slow search results - Inefficient data retrieval - Lack of real-time data analysis

• Benefits:

- Faster search results - More relevant data capture - Real-time data analysis

• Commercial Applications:

Optimized Search Engine Technology for Enhanced User Experience

• Prior Art:

Prior art related to n-gram indexing and log data analysis techniques.

• Frequently Updated Research:

Ongoing research on optimizing search algorithms using n-gram indexing and log data analysis.

Questions about N-Gram Indexing and Log Data Analysis: 1. How does n-gram indexing improve search efficiency? 2. What are the benefits of incorporating recent log data into search algorithms?

Original Abstract Submitted

as described herein, a n-gram index may be created and the search may be conducted using the index, which will lead to faster search results. the n-gram index may also include partial n-gram components to capture more relevant data. moreover, as described herein, the search may also take into account recent log data that has not yet been indexed. techniques for building an index store using log data and efficiently searching the index store and log data to process search requests are described herein.