This post describes performance optimization methods to improve store operations by accelerating equivalent and range searches over PostgreSQL Arrays, JSON and Internal Tag Data.
This post outlines the design and performance of PostgreSQL Similarity Search and describes similarity searches for random texts and arrays to see how PostgreSQL standalone performs.
This post describes how PostgreSQL Image Search Plug-in helps to accelerate image-based searches and also highlights how PostgreSQL helps to screen out duplicate videos.
This article describes the application of PostgreSQL along with algorithms to extract keywords from a document for text (keyword) analysis.
This article provides an overview of the smlar plug-in and describes how it supports multiple similarity algorithms.
This post outlines different methods to find the similarity between documents in various scenarios and focuses on how different algorithms can be used to improve efficiency in such scenarios.
This article explains how the PostgreSQL database along with smlar plug-in helps in efficiently retrieving massive volumes of SimHash data on the basis of the hamming distance.
This post discusses how to calculate the similarity between arrays using algorithms. It focuses on similarity calculation for strings, images, and other types of data in PostgreSQL.
This post outlines several ways to filter duplicate e-commerce content and also describes how indexing helps to determine the similarity between documents.
This post outlines the design and practices for PostgreSQL similarity search distributed architecture and describes how you can perform a parallel query by using DBLink asynchronous calls.
This post describes how can we efficiently search tags and filter records that match the tag's weighted value.
This article describes the methods to accelerate compound queries of single-value fields and multi-value fields over 100 times, focusing on PostgreSQL UDF implementation.
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