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Full-Stack Internet Architecture
Full-Stack Internet Architecture
Sep 4, 2020 · Databases

Comprehensive Guide to Redis Data Structures, Persistence, Transactions, Clustering, and Applications

This article provides an in‑depth technical overview of Redis, covering its core data structures, memory allocation strategies, eviction policies, persistence mechanisms (RDB and AOF), transaction model, sentinel and cluster architectures, Pub/Sub messaging, and multiple approaches to implementing distributed locks.

Data StructuresPersistencePubSub
0 likes · 89 min read
Comprehensive Guide to Redis Data Structures, Persistence, Transactions, Clustering, and Applications
DataFunTalk
DataFunTalk
Jul 20, 2020 · Artificial Intelligence

Embedding Techniques in Tencent Mobile News Recommendation System

This article reviews the practical use of embedding technologies in Tencent's mobile news recommendation pipeline, covering the fundamentals of embeddings, their historical development, item and image embeddings, user embeddings, various vector‑based recall methods, clustering strategies, and recent advances and challenges.

Deep LearningEmbeddingTencent
0 likes · 15 min read
Embedding Techniques in Tencent Mobile News Recommendation System
Laravel Tech Community
Laravel Tech Community
Jul 15, 2020 · Databases

Comprehensive Redis Interview Questions and Answers

This article provides a comprehensive overview of Redis, covering its definition, advantages over memcached, supported data types, memory consumption, eviction policies, clustering options, persistence mechanisms, distributed lock implementations, cache penetration and avalanche solutions, and best-use scenarios compared to other caching systems.

CachePersistenceclustering
0 likes · 26 min read
Comprehensive Redis Interview Questions and Answers
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Apr 26, 2020 · Backend Development

How to Build a Million‑Message‑Per‑Second RabbitMQ Cluster: Lessons from Google

This article explains how to design and scale a RabbitMQ cluster capable of handling millions of messages per second, covering core concepts, Google’s large‑scale test setup, sharding and federation plugins, mirror queues, reliability mechanisms, and practical tips for high‑availability and performance optimization.

Message QueueRabbitMQclustering
0 likes · 25 min read
How to Build a Million‑Message‑Per‑Second RabbitMQ Cluster: Lessons from Google
Yanxuan Tech Team
Yanxuan Tech Team
Apr 20, 2020 · Artificial Intelligence

How AI-Driven Clustering Boosts Smart Customer Service Knowledge Bases

This article outlines an AI-powered workflow for constructing and enriching a business knowledge base in intelligent customer service, covering preprocessing, intent detection, deep and shallow semantic feature engineering, hierarchical bucket clustering, and automated summary extraction to improve FAQ coverage and reduce manual workload.

AIKnowledge BaseNLP
0 likes · 15 min read
How AI-Driven Clustering Boosts Smart Customer Service Knowledge Bases
21CTO
21CTO
Mar 5, 2020 · Fundamentals

How Alibaba Overcame Three Major Challenges in Code Defect Detection with PRECFIX

This article explains how Alibaba's Cloud R&D team tackled the complex business environment, limited auxiliary resources, and strict product requirements of defect detection by developing the PRECFIX method, which extracts, clusters, and templates defect‑repair pairs to improve code review and patch recommendation.

Code reviewSoftware Engineeringclustering
0 likes · 17 min read
How Alibaba Overcame Three Major Challenges in Code Defect Detection with PRECFIX
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Dec 3, 2019 · Databases

How to Build a High‑Performance InfluxDB Cluster for Massive Time‑Series Data

This article explores InfluxDB’s time‑series strengths, compares TSDB with traditional databases, explains its TSM storage engine and shard concepts, and details the design, architecture, performance benchmarks, integration steps, and future enhancements of a high‑availability InfluxDB‑HA solution used at 360.

HighAvailabilityInfluxDBTimeSeriesDatabase
0 likes · 9 min read
How to Build a High‑Performance InfluxDB Cluster for Massive Time‑Series Data
DataFunTalk
DataFunTalk
Nov 25, 2019 · Artificial Intelligence

Real-time Attention-based Look-alike Model for Recommender Systems

This talk presents a real-time attention-based look‑alike model (RALM) designed to address the long‑tail problem in recommendation systems by efficiently expanding seed users, leveraging user representation learning, attention mechanisms, and clustering to deliver timely, diverse content without retraining the model.

Long Tailattentionclustering
0 likes · 24 min read
Real-time Attention-based Look-alike Model for Recommender Systems
Xianyu Technology
Xianyu Technology
Nov 7, 2019 · Big Data

Sequence Pattern Mining for User Behavior Analysis in Xianyu

By applying sequence pattern mining and unsupervised clustering to Xianyu’s massive event logs, the study abstracts high‑level user behaviors, discovers frequent subsequences, uncovers unknown fraudulent account patterns, expands known fraud cohorts with 99 % precision, and enables richer analyses such as PCA‑based cross‑group comparisons.

Big Dataclusteringdata mining
0 likes · 8 min read
Sequence Pattern Mining for User Behavior Analysis in Xianyu
Java Captain
Java Captain
Apr 24, 2019 · Databases

Understanding Redis Data Structures, Clustering, and Core Operations

This article explains how Redis stores all values as byte arrays, clarifies the five primary data structures, describes cluster slot mapping and node‑key relationships, and covers single‑threaded execution, transactions, pipelines, and the Redis protocol in detail.

Data StructuresTransactionsclustering
0 likes · 14 min read
Understanding Redis Data Structures, Clustering, and Core Operations
Java Captain
Java Captain
Apr 8, 2019 · Backend Development

RabbitMQ: Use Cases, Roles, Components, and Operational Practices

This article explains RabbitMQ's typical scenarios, key roles and components, virtual host purpose, message delivery process, durability and loss‑prevention mechanisms, broadcast types, delayed queues, clustering benefits, node types, setup considerations, and cluster shutdown order.

BackendRabbitMQarchitecture
0 likes · 9 min read
RabbitMQ: Use Cases, Roles, Components, and Operational Practices
JD Tech Talk
JD Tech Talk
Mar 22, 2019 · Artificial Intelligence

Data Mining Techniques for Telemarketing: Supervised Classification, Clustering, Optimization, Anomaly Detection, and Text Mining

The article examines how telemarketing, a data‑intensive industry, leverages various data‑mining methods—including supervised classification, clustering, operations research optimization, anomaly detection, and text mining—to improve lead selection, agent allocation, churn prediction, and voice analysis, while also outlining the key data‑talent roles needed for successful implementation.

Telemarketinganomaly detectionclustering
0 likes · 7 min read
Data Mining Techniques for Telemarketing: Supervised Classification, Clustering, Optimization, Anomaly Detection, and Text Mining
MaGe Linux Operations
MaGe Linux Operations
Mar 8, 2019 · Operations

Mastering High‑Availability Clusters: Resources, Constraints, and Failure Handling

This article explains the principles and components of high‑availability (HA) clusters, covering active/standby nodes, resource stickiness and constraints, heartbeat and quorum mechanisms, split‑brain avoidance, failure detection methods, and the minimal setup required for a reliable web‑service HA deployment.

HeartbeatOperationsResource Management
0 likes · 14 min read
Mastering High‑Availability Clusters: Resources, Constraints, and Failure Handling
Efficient Ops
Efficient Ops
Feb 11, 2019 · Databases

Best Redis Cluster Options: Client Sharding, Proxy, Codis, Official

Redis, a high‑performance NoSQL database, offers multiple clustering approaches—including client‑side sharding, proxy‑based solutions like Twemproxy and Codis, and the native Redis Cluster—each with distinct trade‑offs in scalability, availability, operational complexity, and performance, guiding engineers to select the optimal architecture for their workloads.

CodisProxyclustering
0 likes · 15 min read
Best Redis Cluster Options: Client Sharding, Proxy, Codis, Official
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 22, 2019 · Artificial Intelligence

How Tmall’s “Most Concerned” Feature Uses AI to Match Reviews with Consumer Questions

The article explains how Tmall’s new “Most Concerned” module leverages NLP techniques, fastText embeddings, Bi‑LSTM classifiers, and a custom clustering algorithm to filter, group, and link consumer questions with relevant product reviews, improving the shopping experience across many product categories.

AINLPclustering
0 likes · 9 min read
How Tmall’s “Most Concerned” Feature Uses AI to Match Reviews with Consumer Questions
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 2, 2019 · Artificial Intelligence

How AI Detects Screenshot Bugs: From CNN Models to Image Clustering

Leveraging TensorFlow's CNN and OCR‑LSTM models, this article details how AI can automatically spot blank pages, UI anomalies, and garbled text in app screenshots, and describes a Jenkins‑driven retraining pipeline and hierarchical clustering to de‑duplicate images and boost manual review efficiency.

AICNNOCR
0 likes · 7 min read
How AI Detects Screenshot Bugs: From CNN Models to Image Clustering
Xianyu Technology
Xianyu Technology
Dec 12, 2018 · Big Data

Community Data Normalization Using Prefix Matching and Text Similarity

The study presents a four‑step pipeline that normalizes community data for rental platforms by clustering records using longest‑common‑prefix patterns, geographic filtering, Levenshtein similarity, and pattern‑based parent‑child assignment, achieving under 8 % false positives and 5 % false negatives.

GeospatialReal Estateclustering
0 likes · 10 min read
Community Data Normalization Using Prefix Matching and Text Similarity
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 10, 2018 · Databases

Comprehensive Redis Interview Guide: Data Types, Core Features, Persistence, Clustering, and Performance Tips

This article provides an extensive overview of Redis for interview preparation, covering its supported data structures, key functionalities such as Sentinel, replication, transactions, Lua scripting, persistence mechanisms, clustering options, performance characteristics, memory‑optimization strategies, and common use‑case scenarios.

clusteringdata-types
0 likes · 12 min read
Comprehensive Redis Interview Guide: Data Types, Core Features, Persistence, Clustering, and Performance Tips
360 Quality & Efficiency
360 Quality & Efficiency
Dec 7, 2018 · Artificial Intelligence

Image Feature Extraction and Clustering for Key Frame Selection in Mobile App Installation Screenshots

This article presents a technical solution for extracting representative key frames from time‑series screenshots of a mobile app installation process, covering pixel sampling, dimensionality reduction, classic feature extractors (SIFT, HOG, ORB), auto‑encoder based deep learning, and clustering methods such as KMeans and DBSCAN, along with practical results and performance analysis.

AutoencoderComputer VisionHOG
0 likes · 5 min read
Image Feature Extraction and Clustering for Key Frame Selection in Mobile App Installation Screenshots
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Dec 6, 2018 · Databases

Redis vs Memcached: Which In‑Memory Cache Wins for Complex Data?

Redis and Memcached differ significantly in data structure support, memory efficiency, performance, memory management, persistence options, and clustering capabilities, with Redis offering richer data types, server‑side operations, configurable persistence, and native clustering, while Memcached provides simpler key‑value storage, slab allocation, and client‑side distribution.

Data StructuresIn-Memory CacheMemcached
0 likes · 17 min read
Redis vs Memcached: Which In‑Memory Cache Wins for Complex Data?
Architect's Tech Stack
Architect's Tech Stack
Oct 23, 2018 · Databases

Redis Overview: Features, Data Types, Persistence, Clustering, and Common Interview Questions

This article provides a comprehensive introduction to Redis, covering its core concepts, advantages, data structures, persistence mechanisms, eviction policies, clustering, common usage scenarios, and typical interview questions for developers working with this high‑performance in‑memory key‑value store.

CacheIn-Memory DatabasePersistence
0 likes · 24 min read
Redis Overview: Features, Data Types, Persistence, Clustering, and Common Interview Questions
Big Data and Microservices
Big Data and Microservices
Sep 17, 2018 · Big Data

5 Essential Data Mining Techniques Every Analyst Should Know

This article outlines five widely used data‑mining methods—association rules, classification/tagging, clustering, decision trees, and sequential pattern mining—explaining their principles, real‑world examples, and how they help organizations extract actionable insights from massive datasets.

Big DataDecision TreesSequential Pattern Mining
0 likes · 6 min read
5 Essential Data Mining Techniques Every Analyst Should Know
Meituan Technology Team
Meituan Technology Team
Sep 13, 2018 · Mobile Development

ARKit LBS AR Application for Meituan Dining Experience

Meituan’s dining AR app uses ARKit’s orientation‑tracking configuration and gravity‑and‑heading world alignment to place virtual restaurant cards in the camera view, rendering them with SceneKit billboards, handling overlap via tap‑to‑disperse and K‑Means clustering, and eliminating flicker by disabling depth buffering.

ARARKitLBS
0 likes · 15 min read
ARKit LBS AR Application for Meituan Dining Experience
Java Backend Technology
Java Backend Technology
Jul 14, 2018 · Databases

Redis Deep Dive: Core Concepts, Data Types, and Best Practices

This comprehensive guide explains what Redis is, its advantages over memcached, supported data structures, eviction policies, clustering options, persistence mechanisms, memory optimization techniques, and practical use‑cases such as caching, queues, leaderboards, and pub/sub, providing essential knowledge for developers and architects.

Data StructuresIn-Memory DatabasePersistence
0 likes · 26 min read
Redis Deep Dive: Core Concepts, Data Types, and Best Practices
Programmer DD
Programmer DD
Jun 7, 2018 · Operations

How to Build a High‑Availability RabbitMQ Cluster with Load Balancing

This guide explains the principles behind RabbitMQ clustering, shows how metadata synchronization works, compares design choices, and provides step‑by‑step instructions—including component installation, node configuration, HAProxy load‑balancing setup, and a sample architecture diagram—to create a reliable, scalable RabbitMQ cluster for production use.

HAProxyOperationsclustering
0 likes · 16 min read
How to Build a High‑Availability RabbitMQ Cluster with Load Balancing
Efficient Ops
Efficient Ops
Apr 18, 2018 · Operations

Huawei’s Triple‑Play Model: Advancing AIOps for Massive K8s and Serverless

At the 9th Global Operations Conference, Huawei Cloud’s chief architect Cai Xiaogang presented a three‑pronged AIOps strategy that combines large‑scale Kubernetes management, causal tracing in Serverless environments, multi‑source RCA analysis, and clustering‑based black‑box network packet inspection, showcasing how academia‑industry collaboration accelerates cloud‑native operations.

KubernetesRoot Cause AnalysisServerless
0 likes · 8 min read
Huawei’s Triple‑Play Model: Advancing AIOps for Massive K8s and Serverless
Architects' Tech Alliance
Architects' Tech Alliance
Mar 9, 2018 · Artificial Intelligence

Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos

An in‑depth, visual guide walks readers through the fundamentals of machine learning—distinguishing supervised from unsupervised approaches, explaining dimensionality reduction with PCA, detailing clustering techniques such as hierarchical clustering, K‑Means and DBSCAN, and summarizing core regression and classification algorithms including linear regression, SVM, decision trees, logistic regression, Naïve Bayes, and KNN.

Unsupervised Learningclassificationclustering
0 likes · 11 min read
Master Machine Learning Basics: From PCA to KNN Explained with Visual Demos
Architecture Digest
Architecture Digest
Feb 13, 2018 · Artificial Intelligence

Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages

This article provides a concise overview of fifteen widely used machine learning models—including decision trees, random forests, k‑means, KNN, EM, linear and logistic regression, Naive Bayes, Apriori, Boosting, GBDT, SVM, neural networks, HMM, and CRF—detailing their features, strengths, weaknesses, and typical application scenarios.

Neural Networksclassificationclustering
0 likes · 12 min read
Overview of Common Machine Learning Models: Characteristics, Advantages, and Disadvantages
Hulu Beijing
Hulu Beijing
Feb 8, 2018 · Artificial Intelligence

How Self‑Organizing Maps Work: Key Features, Design Tips & K‑Means Comparison

This article explains the principles, biological inspiration, network structure, training process, design parameters, and practical differences of Self‑Organizing Maps (SOM), an unsupervised neural network used for clustering, visualization, and feature extraction, and compares it with methods like K‑means.

Neural NetworksSelf-Organizing MapUnsupervised Learning
0 likes · 10 min read
How Self‑Organizing Maps Work: Key Features, Design Tips & K‑Means Comparison
MaGe Linux Operations
MaGe Linux Operations
Jan 25, 2018 · Databases

Master Redis: Data Structures, Commands, and Performance Tuning Explained

This comprehensive guide introduces Redis fundamentals, covering its core data structures and essential commands, then delves into performance optimization, high‑availability setups with replication and Sentinel, and scaling strategies using Redis Cluster, providing practical examples and best‑practice recommendations for robust in‑memory data management.

Data StructuresReplicationclustering
0 likes · 29 min read
Master Redis: Data Structures, Commands, and Performance Tuning Explained
Qunar Tech Salon
Qunar Tech Salon
Jan 23, 2018 · Artificial Intelligence

Intelligent Business Zone Planning for Super Bus Service Using DBSCAN Clustering and Convex Hull

The article describes how the Super Bus platform leverages unsupervised DBSCAN clustering and a Graham‑scan convex‑hull algorithm, combined with a data‑center and distributed processing framework, to automatically generate compliant service zones that match user demand while improving efficiency and scalability.

DBSCANUnsupervised Learningclustering
0 likes · 8 min read
Intelligent Business Zone Planning for Super Bus Service Using DBSCAN Clustering and Convex Hull
dbaplus Community
dbaplus Community
Jan 14, 2018 · Backend Development

Mastering Tomcat: Kernel Design, Clustering, and Performance Tuning

This article provides a comprehensive technical guide to Tomcat, covering its kernel implementation principles, server models, distributed clustering strategies, production deployment parameters, JVM tuning, request processing flow, servlet mechanisms, filter chains, Comet and WebSocket modes, as well as performance monitoring and optimization techniques.

BackendJVMJava
0 likes · 18 min read
Mastering Tomcat: Kernel Design, Clustering, and Performance Tuning
Architecture Digest
Architecture Digest
Oct 26, 2017 · Databases

Redis Overview: Architecture, Master‑Slave Replication, Cluster Design, Persistence and Failure Handling

This article provides a comprehensive English overview of Redis, covering its in‑memory key‑value data model, master‑slave replication, cluster topology, installation steps, persistence mechanisms (RDB and AOF), consistency hashing, node failure detection, slave election, and the advantages and drawbacks of using Redis Cluster.

In-Memory DatabasePersistenceReplication
0 likes · 16 min read
Redis Overview: Architecture, Master‑Slave Replication, Cluster Design, Persistence and Failure Handling
21CTO
21CTO
Jul 8, 2017 · Artificial Intelligence

Mastering Recommendation Systems: From Collaborative Filtering to Deep Learning

This article surveys major recommendation system techniques—from collaborative filtering and matrix factorization to clustering and deep‑learning approaches like YouTube’s two‑stage neural network—explaining their principles, strengths, and practical considerations for building effective personalized recommenders.

Deep LearningRecommendation SystemsYouTube
0 likes · 10 min read
Mastering Recommendation Systems: From Collaborative Filtering to Deep Learning
MaGe Linux Operations
MaGe Linux Operations
May 7, 2017 · Artificial Intelligence

Big Data & Machine Learning: Core Definitions and Essential Algorithms

This article explains what big data and machine learning are, their interrelationship, various big‑data analysis approaches, core machine‑learning concepts, and details ten fundamental algorithms—including regression, neural networks, SVM, clustering, dimensionality reduction, and recommendation—while highlighting their roles in modern data‑driven applications.

Big DataNeural Networksclustering
0 likes · 24 min read
Big Data & Machine Learning: Core Definitions and Essential Algorithms
Tencent Cloud Developer
Tencent Cloud Developer
Dec 26, 2016 · Databases

Analysis of Redis Design: Network Model, Data Structures, Memory Management, Persistence, and Clustering

The article dissects Redis’s architecture by examining its single‑threaded reactor network model, core data structures and memory‑management tactics, AOF/RDB persistence mechanisms, and master‑slave, Sentinel, and Cluster multi‑node strategies, highlighting how each design choice balances speed, memory usage, and system complexity.

Event-drivenMemory ManagementPersistence
0 likes · 16 min read
Analysis of Redis Design: Network Model, Data Structures, Memory Management, Persistence, and Clustering
Architects' Tech Alliance
Architects' Tech Alliance
Nov 24, 2016 · Big Data

Data Mining Overview: Process, Techniques, and Model Evaluation

This article provides a comprehensive introduction to data mining, covering its definition, goal setting, data sampling, exploration, preprocessing, pattern discovery, model building, evaluation methods, and the main analytical techniques such as classification, regression, clustering, association rules, feature and deviation analysis, and web mining.

Model Evaluationassociation rulesclassification
0 likes · 10 min read
Data Mining Overview: Process, Techniques, and Model Evaluation
Architect
Architect
Mar 6, 2016 · Big Data

Clustering Geolocated User Events with DBSCAN and Spark

This article explains how to apply the DBSCAN clustering algorithm to geolocated user event data and leverage Apache Spark’s distributed processing with PairRDDs to efficiently identify frequent user regions, detect outliers, and build location‑based services such as personalized recommendations and security alerts.

Big DataDBSCANSpark
0 likes · 8 min read
Clustering Geolocated User Events with DBSCAN and Spark
Qunar Tech Salon
Qunar Tech Salon
Feb 6, 2016 · Big Data

An Introduction to Data Mining Algorithms and Their Real-World Applications

This article introduces the main types of data‑mining algorithms—classification, prediction, clustering, and association—explains supervised and unsupervised learning, and illustrates each with practical examples such as spam detection, tumor cell identification, wine quality assessment, fraud detection, recommendation systems, and more.

association analysisclassificationclustering
0 likes · 15 min read
An Introduction to Data Mining Algorithms and Their Real-World Applications
dbaplus Community
dbaplus Community
Dec 25, 2015 · Artificial Intelligence

Detecting Fraudulent ModemPOOL Terminals with K‑Means Clustering

This article details how telecom operators can identify fraudulent ModemPOOL (cat‑pool) terminals and predict churn using data‑driven clustering and day‑interval warning models, covering metric selection, data exploration, k‑means clustering, model deployment, and performance evaluation.

K-MeansModel DeploymentRFM
0 likes · 18 min read
Detecting Fraudulent ModemPOOL Terminals with K‑Means Clustering