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Query Expansion

23 articles · Page 1 of 1
StarRocks
StarRocks
Sep 2, 2026 · Databases

Why AI-Generated SQL Isn't Enough: Semantic Views Bridge the Business Logic Gap

This article explains why syntactically correct AI-generated SQL often fails to reflect business logic, introduces StarRocks' Semantic View as an executable 'information contract' that defines metrics, relationships, and synonyms, and demonstrates with a fishing gear example how it enables deterministic SQL expansion for both AI agents and BI tools.

AI-generated SQLData ModelingInformation Contract
0 likes · 15 min read
Why AI-Generated SQL Isn't Enough: Semantic Views Bridge the Business Logic Gap
Top Architect
Top Architect
Jun 5, 2026 · Databases

Eliminate LIKE% in MySQL: Use Full‑Text Search for Efficient Fuzzy Queries

This article explains why using LIKE% for fuzzy searches in MySQL is inefficient, introduces InnoDB full‑text search (available since MySQL 5.6), describes inverted indexes, shows how to create and query full‑text indexes with natural language, boolean, and query‑expansion modes, discusses relevance calculation, stopwords, token‑size parameters, and provides the syntax for dropping full‑text indexes.

Boolean ModeInnoDBMySQL
0 likes · 12 min read
Eliminate LIKE% in MySQL: Use Full‑Text Search for Efficient Fuzzy Queries
AI Engineer Programming
AI Engineer Programming
May 16, 2026 · Artificial Intelligence

How to Boost RAG Retrieval Quality: Real‑World Cost‑Benefit Analysis

This article examines practical ways to improve Retrieval‑Augmented Generation (RAG) retrieval quality—covering vector database choices, data chunking, embedding models, query expansion, and re‑ranking—while weighing performance gains against operational costs through multiple real‑world case studies.

LLMQuery ExpansionRAG
0 likes · 16 min read
How to Boost RAG Retrieval Quality: Real‑World Cost‑Benefit Analysis
macrozheng
macrozheng
Jan 15, 2026 · Databases

Master MySQL Full-Text Search: Inverted Index, Queries, and Best Practices

This guide explains why InnoDB fuzzy queries lose indexes, introduces MySQL full‑text search with inverted indexes, shows how to create and use full‑text indexes via CREATE TABLE and ALTER statements, and demonstrates natural language, boolean, and query‑expansion modes with practical SQL examples.

Boolean ModeMySQLQuery Expansion
0 likes · 12 min read
Master MySQL Full-Text Search: Inverted Index, Queries, and Best Practices
Architect's Tech Stack
Architect's Tech Stack
Jul 8, 2025 · Databases

Master MySQL Full-Text Search: Indexes, Queries, and Advanced Techniques

This article explains MySQL's InnoDB full‑text search, covering the theory of inverted indexes, how to create and use full‑text indexes with various MATCH…AGAINST modes, Boolean operators, query expansion, relevance calculation, and index removal, illustrated with practical SQL examples and diagrams.

MySQLQuery ExpansionSQL
0 likes · 12 min read
Master MySQL Full-Text Search: Indexes, Queries, and Advanced Techniques
JD Tech
JD Tech
Jun 16, 2025 · Artificial Intelligence

How JD Engineers Leverage LLMs and Sparse Models to Boost Search and Ads

This article showcases three JD tech case studies—using large language models for e‑commerce query expansion, applying sparse large models with scaling‑law experiments to improve ad prediction, and building proactive risk‑prevention systems—to illustrate practical AI engineering that drives higher recall, conversion, and system robustness.

AdvertisingQuery Expansione-commerce
0 likes · 8 min read
How JD Engineers Leverage LLMs and Sparse Models to Boost Search and Ads
JD Cloud Developers
JD Cloud Developers
May 27, 2025 · Artificial Intelligence

How JD’s Young AI Engineers Tackle Real-World Model Challenges

Young JD algorithm engineers share how they solve tough AI problems—from optimizing large‑model training and reward‑model design for ad image generation, to building LLM‑based query expansion, agent evaluation, and model pruning with FFT and RDP—illustrating practical breakthroughs and personal growth in cutting‑edge AI research.

AIAlgorithm EngineeringModel Pruning
0 likes · 15 min read
How JD’s Young AI Engineers Tackle Real-World Model Challenges
JD Tech
JD Tech
May 26, 2025 · Artificial Intelligence

Solving Technical Challenges at JD Retail: Multi‑Reward Models, LLM‑Based Query Expansion, Model Pruning, and Reinforcement Learning

This article details how JD Retail's young algorithm engineers tackled a series of AI engineering problems—including advertising image quality assessment with multi‑reward models, large‑language‑model‑driven query expansion, FFT‑and‑RDP‑based model pruning, and agent‑centric reinforcement learning—while sharing practical growth insights and code snippets.

AIQuery ExpansionReinforcement Learning
0 likes · 15 min read
Solving Technical Challenges at JD Retail: Multi‑Reward Models, LLM‑Based Query Expansion, Model Pruning, and Reinforcement Learning
JD Retail Technology
JD Retail Technology
May 7, 2025 · Artificial Intelligence

Solving Technical Challenges with Large AI Models at JD Retail: Reward Modeling, Query Expansion, and Model Pruning

JD Retail’s engineering team tackles hard AI problems by replacing a monolithic reward model with specialized small models for ad‑image generation, deploying an LLM‑driven query‑expansion pipeline that lifts conversion rates, and pruning text‑to‑image transformers using FFT and RDP to boost throughput 40% without loss, while building comprehensive evaluation tools and a semantic smart‑assistant.

AIModel PruningQuery Expansion
0 likes · 14 min read
Solving Technical Challenges with Large AI Models at JD Retail: Reward Modeling, Query Expansion, and Model Pruning
Selected Java Interview Questions
Selected Java Interview Questions
Aug 27, 2024 · Databases

MySQL Full‑Text Search: Inverted Index, Query Modes, and Usage

This article explains how MySQL InnoDB implements full‑text search using inverted indexes, shows how to create and drop full‑text indexes, and demonstrates the three query modes—Natural Language, Boolean, and Query Expansion—along with their syntax, operators, relevance calculation, and practical examples.

Boolean ModeMySQLQuery Expansion
0 likes · 11 min read
MySQL Full‑Text Search: Inverted Index, Query Modes, and Usage
Programmer XiaoFu
Programmer XiaoFu
May 16, 2024 · Databases

MySQL Fuzzy Search Without LIKE%: Using Full‑Text Indexes

This article explains how InnoDB full‑text search can replace LIKE% fuzzy queries by using inverted indexes, covering index structures, creation syntax, MATCH() AGAINST() modes, practical demos, query expansion, and index removal, while noting when to prefer external search engines.

Boolean ModeMySQLQuery Expansion
0 likes · 12 min read
MySQL Fuzzy Search Without LIKE%: Using Full‑Text Indexes
Liangxu Linux
Liangxu Linux
Feb 28, 2024 · Databases

Master MySQL InnoDB Full-Text Search: Indexes, Queries, and Advanced Techniques

This article explains how MySQL InnoDB implements full‑text search, covering the underlying inverted index structures, how to create and drop full‑text indexes, the MATCH…AGAINST syntax, and detailed examples of natural language, boolean, and query‑expansion search modes with practical SQL demos.

Boolean ModeMySQLQuery Expansion
0 likes · 12 min read
Master MySQL InnoDB Full-Text Search: Indexes, Queries, and Advanced Techniques
Laravel Tech Community
Laravel Tech Community
Feb 4, 2024 · Databases

Understanding Full-Text Search and Inverted Indexes in MySQL InnoDB

This article explains how MySQL InnoDB implements full‑text search using inverted indexes, covers the creation and usage of FULLTEXT indexes, demonstrates various MATCH…AGAINST query modes such as natural language, boolean, and query expansion, and shows how to manage and delete full‑text indexes with practical SQL examples.

Boolean ModeMySQLQuery Expansion
0 likes · 10 min read
Understanding Full-Text Search and Inverted Indexes in MySQL InnoDB
Architect's Tech Stack
Architect's Tech Stack
Mar 3, 2022 · Databases

Understanding MySQL InnoDB Full-Text Search and Inverted Index

This article explains why traditional B‑Tree indexes fail for keyword searches, introduces the concept of inverted indexes, shows how to create and use MySQL InnoDB full‑text indexes with MATCH‑AGAINST in various modes, and covers index maintenance and query‑expansion techniques.

Boolean ModeMySQLQuery Expansion
0 likes · 12 min read
Understanding MySQL InnoDB Full-Text Search and Inverted Index
Java Backend Technology
Java Backend Technology
Jan 18, 2022 · Databases

Master MySQL Full-Text Search: Inverted Indexes, Query Modes, and Optimization

This article explains how InnoDB implements full-text search using inverted indexes, shows how to create and use full-text indexes with various query modes—including natural language, boolean, and query expansion—covers relevance calculation, stopwords, token size limits, and demonstrates how to delete indexes, all illustrated with SQL examples and diagrams.

Boolean ModeMySQLQuery Expansion
0 likes · 13 min read
Master MySQL Full-Text Search: Inverted Indexes, Query Modes, and Optimization
21CTO
21CTO
Nov 3, 2021 · Databases

Master MySQL Full‑Text Search: Index Creation, Modes, and Internals

This tutorial explains how MySQL implements full‑text search, covering the creation of full‑text indexes (including Chinese ngram support), the three query modes (natural language, boolean, and query expansion), relevance ranking, underlying inverted‑index structures, cache handling, and common DML operations.

Boolean ModeMySQLQuery Expansion
0 likes · 14 min read
Master MySQL Full‑Text Search: Index Creation, Modes, and Internals
Aikesheng Open Source Community
Aikesheng Open Source Community
May 26, 2021 · Databases

Practical Guide to Using MySQL Full-Text Indexes

This article explains MySQL full‑text indexing, compares its syntax with ordinary SQL, demonstrates how to create and query a full‑text index using natural language, boolean, and query‑expansion modes, and shows performance differences through execution‑plan analysis and relevance ranking.

Boolean ModeFull-Text IndexMySQL
0 likes · 9 min read
Practical Guide to Using MySQL Full-Text Indexes
DataFunTalk
DataFunTalk
Dec 14, 2020 · Artificial Intelligence

Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions

This article reviews current query expansion methods, contrasting relevance‑based models that rely on terms or entities with generative models that encode whole queries, discusses challenges of handling long and complex queries, and surveys recent research on encoding queries, session modeling, and multi‑task feature integration.

Generative ModelsInformation RetrievalNLP
0 likes · 9 min read
Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions
DataFunTalk
DataFunTalk
May 21, 2020 · Artificial Intelligence

Query Expansion Techniques for Search Optimization: Models, Data Sources, and Practical Practices

This article reviews the factors influencing search results, explains why query expansion is crucial for improving recall, surveys various sources of expansion terms, describes probabilistic and translation‑based models, and offers practical recommendations for building effective, data‑driven query expansion pipelines.

Information RetrievalQuery ExpansionSearch Optimization
0 likes · 11 min read
Query Expansion Techniques for Search Optimization: Models, Data Sources, and Practical Practices
DataFunTalk
DataFunTalk
Sep 10, 2019 · Artificial Intelligence

Computational Advertising: Overview and Key Techniques from the Second Edition

The article introduces the second edition of "Computational Advertising", highlights its practical coverage of bidding algorithms, eCPM estimation, query expansion methods, and ad placement optimization, while also noting its industry impact, author credentials, and a limited‑time book giveaway.

Ad TechQuery Expansionbidding algorithms
0 likes · 10 min read
Computational Advertising: Overview and Key Techniques from the Second Edition
Baixing.com Technical Team
Baixing.com Technical Team
Sep 11, 2017 · Artificial Intelligence

How Do Search Engines Decode User Intent? Exploring Query Extension Techniques

This article explains how modern search engines identify precise and broad user intents, examines real‑world query examples, and details extension modules such as synonym, pinyin, and correction that enhance query understanding using algorithms like Aho‑Corasick, Hidden Markov Models, and Levenshtein distance.

Information RetrievalNatural Language ProcessingQuery Expansion
0 likes · 10 min read
How Do Search Engines Decode User Intent? Exploring Query Extension Techniques