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anomaly detection

152 articles · Page 2 of 2
Alimama Tech
Alimama Tech
Jul 21, 2021 · Artificial Intelligence

Ad Fraud Detection and Risk Control Practices at Alibaba Mama

Alibaba Mama combats the roughly 8.6 % abnormal traffic in China’s online ad market by distinguishing low‑quality from cheating clicks, employing a proactive perception layer, high‑dimensional visual analytics, and a dual‑stage real‑time and batch filtering system that also freezes fraudulent affiliate commissions and is continuously evaluated with precision‑recall and AUC metrics.

AlibabaOnline Advertisingad fraud detection
0 likes · 15 min read
Ad Fraud Detection and Risk Control Practices at Alibaba Mama
Beijing SF i-TECH City Technology Team
Beijing SF i-TECH City Technology Team
May 17, 2021 · Artificial Intelligence

AIOps Overview: Concepts, Applications, and Case Studies

This article provides a comprehensive overview of AIOps, covering its definition, evolution from manual to AI-driven operations, core capabilities, and real-world applications in capacity prediction, anomaly detection, and alarm merging, illustrated with case studies from a food‑retail giant and internal logistics.

AIOpsArtificial IntelligenceCapacity Prediction
0 likes · 13 min read
AIOps Overview: Concepts, Applications, and Case Studies
Efficient Ops
Efficient Ops
Feb 1, 2021 · Operations

How to Detect Anomalous Nodes in Massive Compute Clusters Using Intelligent Ops

This article explains how internet companies can reduce soaring manual operations costs by applying intelligent monitoring techniques—such as pattern recognition and statistical anomaly detection—to automatically identify abnormal nodes among thousands of servers, streamline fault diagnosis, and improve service quality.

anomaly detectionlarge-scale systemsmonitoring
0 likes · 4 min read
How to Detect Anomalous Nodes in Massive Compute Clusters Using Intelligent Ops
vivo Internet Technology
vivo Internet Technology
Jan 13, 2021 · Big Data

Statistical Monitoring Using Normal Distribution and Boxplot: Theory, Implementation, and API Design

The article explains the origin of the normal distribution, the central limit theorem, and how boxplots identify anomalies, then describes a Java‑based API that partitions data into five median‑centered levels using same‑period and year‑over‑year ratios to automatically detect and classify abnormal trends in daily metrics.

BoxplotJavaanomaly detection
0 likes · 11 min read
Statistical Monitoring Using Normal Distribution and Boxplot: Theory, Implementation, and API Design
Suning Technology
Suning Technology
Aug 29, 2020 · Artificial Intelligence

How AI Powers Large‑Scale Time Series Forecasting and Root‑Cause Analysis

This article describes Suning's AI‑driven end‑to‑end solution for massive time‑series monitoring, anomaly detection, forecasting with DeepAR, MQ‑RNN, MQ‑CNN, ensemble methods, root‑cause localization using Hotspot and Monte‑Carlo Tree Search, and the evolution of its large‑scale log analytics platform.

Log AnalyticsTime Series Forecastinganomaly detection
0 likes · 17 min read
How AI Powers Large‑Scale Time Series Forecasting and Root‑Cause Analysis
Bitu Technology
Bitu Technology
Aug 28, 2020 · Artificial Intelligence

KPI Forecasting and Anomaly Detection at Tubi Using Prophet

This article describes how Tubi’s data science team built a robust KPI forecasting system with Facebook’s Prophet, covering visualization dashboards, anomaly detection, feature engineering, PySpark deployment, and evaluation using Brier scores to improve business decision‑making.

Brier scoreKPIProphet
0 likes · 13 min read
KPI Forecasting and Anomaly Detection at Tubi Using Prophet
Python Programming Learning Circle
Python Programming Learning Circle
May 21, 2020 · Artificial Intelligence

Time Series Forecasting and Anomaly Detection for API Traffic Using Seasonal Decomposition and ARIMA

The article presents a complete workflow for predicting next‑day API request volumes by exploring per‑minute traffic data, handling missing values, applying seasonal decomposition, training an ARIMA model on the trend component, and generating confidence intervals to flag anomalous spikes.

ARIMATime Seriesanomaly detection
0 likes · 12 min read
Time Series Forecasting and Anomaly Detection for API Traffic Using Seasonal Decomposition and ARIMA
dbaplus Community
dbaplus Community
Apr 6, 2020 · Databases

How AI‑Driven Intelligent Ops Transform Database Management in Banking

This article examines the severe time‑critical pain points of bank database operations, explains why AI‑based intelligent ops are needed, describes the platform architecture, unsupervised algorithms (3σ, Isolation Forest, DBSCAN, Pearson, Apriori), and presents a real‑world case study that demonstrates anomaly detection, root‑cause analysis, and practical optimization recommendations.

AIOpsContainerizationPython
0 likes · 23 min read
How AI‑Driven Intelligent Ops Transform Database Management in Banking
dbaplus Community
dbaplus Community
Mar 30, 2020 · Databases

Intelligent Database Ops: Expert Answers on Monitoring, Prediction & Automation

In a detailed Q&A session, Minsheng Bank’s database specialist shares 16 practical insights on intelligent database operations, covering data storage choices, monitoring tools like Zabbix and Prometheus, anomaly detection, prediction modeling, SQL metric collection, log analysis, and the evolving role of DBAs in the era of AI‑driven ops.

DBAPredictive Modelinganomaly detection
0 likes · 10 min read
Intelligent Database Ops: Expert Answers on Monitoring, Prediction & Automation
dbaplus Community
dbaplus Community
Mar 9, 2020 · Artificial Intelligence

How LSTM‑Powered Real‑Time Alerting with Spark Streaming Boosts Ops Efficiency

This article details a deep‑learning‑driven, real‑time alert system that combines TensorFlow LSTM time‑series forecasting with Spark Streaming to achieve high‑coverage, low‑latency anomaly detection for large‑scale data‑ops environments, including data preprocessing, metric classification, model training, and deployment pipelines.

AI OpsLSTMSpark Streaming
0 likes · 18 min read
How LSTM‑Powered Real‑Time Alerting with Spark Streaming Boosts Ops Efficiency
Efficient Ops
Efficient Ops
Feb 18, 2020 · Operations

How Intelligent Ops Transforms Monitoring: Multi‑Dimensional Anomaly Detection & Smart Alert Merging

This article presents the 2019 GOPS Global Operations Conference talk by Gong Cheng, detailing how intelligent monitoring leverages multi‑dimensional anomaly detection, machine‑learning‑based alert merging, knowledge‑graph construction, and root‑cause analysis to automate and improve large‑scale IT operations.

alert merginganomaly detectionknowledge graph
0 likes · 22 min read
How Intelligent Ops Transforms Monitoring: Multi‑Dimensional Anomaly Detection & Smart Alert Merging
DataFunTalk
DataFunTalk
Feb 10, 2020 · Artificial Intelligence

Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)

The article describes Ctrip's Prophet platform, which combines Flink real‑time stream processing with TensorFlow deep‑learning models to provide intelligent, low‑latency anomaly detection, replacing traditional rule‑based alerts and addressing challenges such as holiday traffic and model scalability.

AIFlinkTensorFlow
0 likes · 13 min read
Real‑Time Intelligent Anomaly Detection Platform at Ctrip: Integrating Flink and TensorFlow (Prophet)
Alibaba Cloud Developer
Alibaba Cloud Developer
Jan 16, 2020 · Artificial Intelligence

How Alibaba DAMO Academy Revolutionizes Anomaly Detection for Business and Machine Data

This article explains the evolution of Alibaba DAMO Academy’s time‑series anomaly detection technology, detailing its application to both machine and commercial data, the challenges of diverse data types, the new robust statistical models, automatic data classification, parameter recommendation, and real‑world case studies demonstrating improved accuracy and stability.

AlibabaData AnalyticsTime Series
0 likes · 14 min read
How Alibaba DAMO Academy Revolutionizes Anomaly Detection for Business and Machine Data
21CTO
21CTO
Dec 3, 2019 · Operations

Why Most Alerts Fail and How to Build Actionable Monitoring

This article explains why many system alerts are poorly designed, describes the true purpose of alerts as actionable notifications, distinguishes business rule monitoring from reliability monitoring, and presents practical metrics, strategies, and simple anomaly‑detection algorithms to create high‑quality, actionable alerts for reliable operations.

alertinganomaly detectionmetrics
0 likes · 23 min read
Why Most Alerts Fail and How to Build Actionable Monitoring
58 Tech
58 Tech
Nov 4, 2019 · Operations

Intelligent Operations Practices: Multi‑Dimensional Anomaly Detection, Alarm Merging, Knowledge‑Graph Construction, and Root‑Cause Analysis

This article summarizes the keynote on intelligent operations presented at the 13th GOPS Global Operations Conference, covering multi‑dimensional anomaly detection, smart alarm aggregation, the construction of an operations knowledge graph, and AI‑driven root‑cause analysis techniques for large‑scale server environments.

alarm merginganomaly detectionintelligent monitoring
0 likes · 9 min read
Intelligent Operations Practices: Multi‑Dimensional Anomaly Detection, Alarm Merging, Knowledge‑Graph Construction, and Root‑Cause Analysis
Tencent Database Technology
Tencent Database Technology
Sep 26, 2019 · Artificial Intelligence

Understanding X‑Pack Machine Learning in Elasticsearch: Features, Architecture, and Implementation

This article explains Elasticsearch X‑Pack's machine‑learning capabilities, covering supervised and unsupervised learning concepts, data preparation, task creation types, architecture components, data flow, result indices, and provides code examples for configuring and running ML jobs.

Data VisualizationElasticsearchTime Series
0 likes · 16 min read
Understanding X‑Pack Machine Learning in Elasticsearch: Features, Architecture, and Implementation
58 Tech
58 Tech
Aug 29, 2019 · Information Security

Graph-Based Anomaly Detection Framework for Security Threats

The article presents a graph‑based anomaly detection architecture that tackles black‑market resource switching by constructing complex user‑traffic networks, mining graph similarities, and applying multi‑dimensional strategies to achieve high‑accuracy detection while meeting timeliness, performance, and interpretability requirements.

anomaly detectionbehavior analysisbig data
0 likes · 8 min read
Graph-Based Anomaly Detection Framework for Security Threats
Efficient Ops
Efficient Ops
Jul 28, 2019 · Operations

How 58’s Intelligent Monitoring System Guarantees 24/7 Service Stability

This article details the design, architecture, and AI‑driven features of 58’s intelligent monitoring platform, explaining how multi‑dimensional data collection, predictive analytics, and smart alarm merging ensure continuous, automated observability across network, server, application, and business layers.

Cloud Infrastructureanomaly detectionmachine learning
0 likes · 20 min read
How 58’s Intelligent Monitoring System Guarantees 24/7 Service Stability
Programmer DD
Programmer DD
Jun 7, 2019 · Operations

Why Most Alerts Fail and How to Build Actionable Monitoring

This article explains the fundamental flaws of typical alert systems, distinguishes between business rule and reliability monitoring, outlines essential metrics and strategies for effective alerts, and presents simple yet powerful anomaly‑detection algorithms to ensure alerts are actionable and reduce noise.

alertinganomaly detectionoperations
0 likes · 21 min read
Why Most Alerts Fail and How to Build Actionable Monitoring
Alibaba Cloud Developer
Alibaba Cloud Developer
May 22, 2019 · Artificial Intelligence

Mastering Anomaly Detection: From Moving Averages to Isolation Forests

This comprehensive guide explores a wide range of anomaly detection techniques—including time‑series methods, statistical models, distance‑based approaches, tree‑based isolation forests, graph algorithms, behavior‑sequence Markov models, and supervised machine‑learning models—detailing their principles, formulas, and practical scenarios for detecting outliers in advertising, fraud, and system monitoring.

Graph AlgorithmsIsolation ForestTime Series
0 likes · 19 min read
Mastering Anomaly Detection: From Moving Averages to Isolation Forests
37 Interactive Technology Team
37 Interactive Technology Team
Apr 28, 2019 · Artificial Intelligence

Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring

By applying XGBoost‑based regression models that are retrained daily on two‑week order data and tuned per sub‑package, the mobile‑game recharge monitoring system predicts 10‑minute order volumes, sharply cuts false alarms from hundreds to dozens, and delivers precise, scalable alerts for game operations.

Mobile GamingXGBoostanomaly detection
0 likes · 8 min read
Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring
Ctrip Technology
Ctrip Technology
Apr 11, 2019 · Artificial Intelligence

An Overview of Anomaly Detection Methods and Their Applications

This article introduces the concept of anomaly detection, outlines common application scenarios such as ELT pipelines, feature engineering, A/B testing, and fraud detection, and reviews various detection methods—including statistical models, machine learning, rule‑based logic, and density‑based techniques—while discussing practical implementation considerations.

Data QualityTime Seriesanomaly detection
0 likes · 12 min read
An Overview of Anomaly Detection Methods and Their Applications
Efficient Ops
Efficient Ops
Mar 31, 2019 · Operations

How to Design Actionable Alerts and Effective Monitoring Strategies

This article explains why most alerts are poorly designed, defines actionable alerts, outlines monitoring objectives, discusses metric selection, and presents simple yet powerful algorithms for anomaly detection to improve system reliability and operational efficiency.

alert designanomaly detectionmetrics
0 likes · 21 min read
How to Design Actionable Alerts and Effective Monitoring Strategies
58 Tech
58 Tech
Mar 25, 2019 · Artificial Intelligence

Machine Learning‑Based Threshold‑Free Monitoring for Business Metrics

This article describes a monitoring system that leverages machine learning to perform threshold‑free, real‑time anomaly detection on macro business indicators such as network traffic and access volume, detailing its architecture, sample labeling, model training, and multi‑level alarm strategies.

AIBusiness Metricsanomaly detection
0 likes · 7 min read
Machine Learning‑Based Threshold‑Free Monitoring for Business Metrics
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.

ClusteringOptimizationTelemarketing
0 likes · 7 min read
Data Mining Techniques for Telemarketing: Supervised Classification, Clustering, Optimization, Anomaly Detection, and Text Mining
Alibaba Cloud Developer
Alibaba Cloud Developer
Mar 19, 2019 · Artificial Intelligence

Unlocking Anomaly Detection: Techniques from Time Series to Deep Learning

This comprehensive guide explores anomaly (outlier) detection across diverse methods—including time‑series analysis, statistical tests, distance metrics, matrix factorization, graph approaches, behavior‑sequence modeling, and supervised machine‑learning models—highlighting their principles, formulas, and practical use cases such as fraud prevention and system monitoring.

Time Seriesanomaly detectiondeep learning
0 likes · 17 min read
Unlocking Anomaly Detection: Techniques from Time Series to Deep Learning
58 Tech
58 Tech
Feb 21, 2019 · Artificial Intelligence

Threshold‑Free Business Metric Monitoring Using Machine Learning

This article describes how a machine‑learning‑driven monitoring system replaces fixed thresholds with personalized, anomaly‑based detection for business‑level metrics such as network traffic and access volume, detailing the architecture, sample labeling, model training, alarm grading, and operational benefits.

AI OpsBusiness Metricsalarm grading
0 likes · 8 min read
Threshold‑Free Business Metric Monitoring Using Machine Learning
dbaplus Community
dbaplus Community
Dec 25, 2018 · Operations

How Ctrip Leverages AI to Revolutionize Application Operations: AIOps Practices and Insights

This article details Ctrip's journey of applying AI-driven AIOps to address application operation pain points, describing their evolution from manual scripts to intelligent automation, the implementation of anomaly detection, smart diagnosis, online/offline mixed deployment, and future considerations for scalable, cost‑effective operations.

AIOpsOnline/Offline Deploymentanomaly detection
0 likes · 29 min read
How Ctrip Leverages AI to Revolutionize Application Operations: AIOps Practices and Insights
Ctrip Technology
Ctrip Technology
Dec 19, 2018 · Artificial Intelligence

Anomaly Detection in Operations: From Statistical Rules to Deep Learning with LSTM and Real‑Time Streaming

This article describes how a large‑scale operations monitoring system at Ctrip evolved from rule‑based alerts to a deep‑learning‑driven anomaly detection pipeline using LSTM models and Flink streaming, achieving a ten‑fold reduction in alert volume while improving fault detection.

FlinkLSTManomaly detection
0 likes · 14 min read
Anomaly Detection in Operations: From Statistical Rules to Deep Learning with LSTM and Real‑Time Streaming
Efficient Ops
Efficient Ops
Dec 11, 2018 · Operations

How Alibaba’s AI‑Powered Monitoring Tackles Complex Business Anomalies

In this talk, Alibaba senior tech expert Wang Zhaogang explains how intelligent monitoring, powered by machine‑learning algorithms and multi‑metric analysis, addresses the challenges of diverse business scenarios, enhances anomaly detection, improves root‑cause analysis, and shapes the future of smart operations.

anomaly detectionintelligent monitoringmachine learning
0 likes · 23 min read
How Alibaba’s AI‑Powered Monitoring Tackles Complex Business Anomalies
360 Tech Engineering
360 Tech Engineering
Nov 22, 2018 · Artificial Intelligence

AIOps Practices at 360: Cost Reduction, Efficiency Gains, and Intelligent Operations

This article presents 360's AIOps project, detailing how AI-driven capacity forecasting, host classification, resource recycling, intelligent MySQL scheduling, anomaly detection, alarm convergence, and root‑cause analysis have saved millions, improved efficiency, and paved the way for a fully automated operations workflow.

AIOpsCapacity ForecastingCost Optimization
0 likes · 14 min read
AIOps Practices at 360: Cost Reduction, Efficiency Gains, and Intelligent Operations
Alibaba Cloud Developer
Alibaba Cloud Developer
Nov 21, 2018 · Artificial Intelligence

How Unsupervised Autoencoders Boost International Credit Card Fraud Detection

International credit card fraud, a growing threat, can be more effectively identified by applying unsupervised autoencoder models, which outperform traditional rule‑based systems by tripling recall and increasing accuracy by 40%, while reducing maintenance costs and adapting to new fraud patterns.

AutoencoderUnsupervised Learninganomaly detection
0 likes · 9 min read
How Unsupervised Autoencoders Boost International Credit Card Fraud Detection
dbaplus Community
dbaplus Community
Nov 11, 2018 · Operations

How 360 Built an AI‑Powered Ops System to Cut Costs and Boost Efficiency

360’s AI‑ops team shares a year‑long journey of turning massive operational data into intelligent solutions—covering background, their AIOps philosophy, practical modules like capacity forecasting, host classification, resource reclamation, smart MySQL scheduling, anomaly detection, alarm reduction, and root‑cause analysis—to dramatically improve cost, efficiency, and reliability.

AIOpsCapacity Forecastinganomaly detection
0 likes · 16 min read
How 360 Built an AI‑Powered Ops System to Cut Costs and Boost Efficiency
Alibaba Cloud Developer
Alibaba Cloud Developer
May 21, 2018 · Databases

How TcpRT Enables Real‑Time Service Quality Monitoring for Massive Cloud Databases

TcpRT is a real‑time instrumentation and diagnostic system for Alibaba Cloud RDS that non‑intrusively collects TCP trace data, aggregates billions of records per day, applies statistical and Cauchy‑based anomaly detection, and pinpoints root causes across hosts, proxies, and network devices at massive scale.

SIGMODanomaly detectioncloud databases
0 likes · 27 min read
How TcpRT Enables Real‑Time Service Quality Monitoring for Massive Cloud Databases
Tencent Cloud Developer
Tencent Cloud Developer
May 9, 2018 · Artificial Intelligence

From Mathematics to Machine Learning: A Personal Journey Through Recommendation, Security, and AIOps

A mathematician‑turned‑engineer recounts his 2015‑2022 path from undocumented recommendation systems at Tencent, through high‑precision security models, reinforcement‑learning game AI, quantum‑ML studies, to large‑scale AIOps time‑series anomaly detection, offering practical lessons for anyone transitioning into machine learning.

AIOpsLogistic RegressionSQL
0 likes · 16 min read
From Mathematics to Machine Learning: A Personal Journey Through Recommendation, Security, and AIOps
High Availability Architecture
High Availability Architecture
May 9, 2018 · Artificial Intelligence

Building a Data‑Driven Intelligent Operations (AIOps) Platform: Architecture, Core Scenarios, and Open‑Source Tools

This article presents a comprehensive guide to constructing a data‑driven AIOps platform, detailing its architecture, core components such as time‑series forecasting, anomaly detection, and pattern clustering, and recommending open‑source projects and practical considerations for implementing intelligent operations in enterprises.

AIOpsLog ClusteringTime Series Forecasting
0 likes · 13 min read
Building a Data‑Driven Intelligent Operations (AIOps) Platform: Architecture, Core Scenarios, and Open‑Source Tools
Efficient Ops
Efficient Ops
Apr 26, 2018 · Operations

How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms

This article explains how 360’s network operations team uses time‑series analysis, statistical thresholds, EWMA, dynamic limits, and machine‑learning models such as K‑Means and Isolation Forest to automatically detect, locate, and remediate traffic anomalies across massive data‑center exits.

AI OpsTime Seriesanomaly detection
0 likes · 15 min read
How 360 Detects Network Anomalies with AI‑Powered Time‑Series Algorithms
AntTech
AntTech
Apr 24, 2018 · Artificial Intelligence

Anomaly Detection with Partially Observed Anomalies: A Two‑Stage Semi‑Supervised Approach

This article summarizes a two‑stage method for anomaly detection when only a few labeled anomalies and many unlabeled instances are available, detailing problem formulation, isolation‑forest‑based scoring, clustering of anomalies, weighted multiclass modeling, experimental validation, and real‑world URL attack applications.

Pu-Learninganomaly detectionmachine learning
0 likes · 10 min read
Anomaly Detection with Partially Observed Anomalies: A Two‑Stage Semi‑Supervised Approach
Efficient Ops
Efficient Ops
Apr 17, 2018 · Artificial Intelligence

From Math to ML: My Path Through Recommendation, Security, and AIOps

This article chronicles the author’s transition from a mathematics background to machine learning, detailing early challenges, hands‑on projects in recommendation systems, security, and AIOps, and sharing practical insights on feature engineering, model evaluation, and large‑scale anomaly detection.

AIOpsanomaly detectionfeature engineering
0 likes · 17 min read
From Math to ML: My Path Through Recommendation, Security, and AIOps
Architects' Tech Alliance
Architects' Tech Alliance
Mar 16, 2018 · Operations

How Machine Learning Powers Intelligent Operations: Real‑World Baidu Case Studies

This article examines Baidu's practical applications of machine‑learning‑driven intelligent operations, detailing three real‑world scenarios, the challenges of KPI anomaly labeling, the design of an automated detection framework, evaluation results across multiple datasets, and broader insights for scaling AIOps in production environments.

AIOpsBaiduanomaly detection
0 likes · 16 min read
How Machine Learning Powers Intelligent Operations: Real‑World Baidu Case Studies
Efficient Ops
Efficient Ops
Feb 6, 2018 · Operations

Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves

The article recounts the author’s 2017‑onward journey building an intelligent operations platform at Tencent, detailing challenges such as legacy thresholds, AIOps talent shortage, and lack of frameworks, and explains how a two‑stage hybrid unsupervised‑supervised model was devised to automatically detect anomalies across millions of KPI time‑series, enabling scalable root‑cause analysis and cost optimization.

AIOpsTime Seriesanomaly detection
0 likes · 7 min read
Hybrid Learning Beats Thresholds: Anomaly Detection for Millions of KPI Curves
360 Zhihui Cloud Developer
360 Zhihui Cloud Developer
Jan 18, 2018 · Artificial Intelligence

How AIOps Can Transform IT Operations: A Practical Roadmap from Tsinghua’s Pei Dan

This article outlines the challenges of modern IT operations, explains why traditional methods fall short, and presents a detailed AIOps roadmap—including anomaly detection, localization, root‑cause analysis, and prediction—drawn from Professor Pei Dan’s research and real‑world examples.

AIOpsIT Operationsanomaly detection
0 likes · 20 min read
How AIOps Can Transform IT Operations: A Practical Roadmap from Tsinghua’s Pei Dan
WeChat Backend Team
WeChat Backend Team
Jan 18, 2018 · Information Security

How WeChat Detects Anomalous Users at Billion‑Scale: Inside Its Fast, Scalable Framework

This article explains how WeChat’s security team builds a scalable anomaly‑detection framework that partitions billions of user accounts, weights suspicious attributes, computes similarity graphs, and leverages Spark optimizations and graph‑partitioning techniques to efficiently identify malicious user clusters.

Large-Scale GraphSecuritySpark optimization
0 likes · 18 min read
How WeChat Detects Anomalous Users at Billion‑Scale: Inside Its Fast, Scalable Framework
Efficient Ops
Efficient Ops
Jan 7, 2018 · Operations

How Tencent Leverages AI to Simplify Massive-Scale Service Monitoring and Root‑Cause Analysis

Tencent's SNG social platform team tackles billion‑scale traffic by integrating AI‑driven anomaly detection, multi‑dimensional monitoring, and decision‑tree based root‑cause analysis, turning complex backend architectures and massive alert volumes into streamlined, actionable insights for faster issue resolution.

AIanomaly detectiondecision tree
0 likes · 16 min read
How Tencent Leverages AI to Simplify Massive-Scale Service Monitoring and Root‑Cause Analysis
Meituan Technology Team
Meituan Technology Team
Dec 1, 2017 · Big Data

Metric Logic Tree: Automated Anomaly Analysis for Business Metrics

The Metric Logic Tree automates business metric anomaly analysis by integrating heterogeneous data sources (Kylin, MySQL, Elasticsearch, Druid) with a three‑layer architecture—metric calculation, algorithmic analysis (waterfall and Gini‑coefficient methods), and a master‑worker computation service—that parallelizes queries, delivers immediate conclusions, and shortens decision cycles, as demonstrated in Meituan‑Dianping’s hotel‑travel operations.

Data Pipelinealgorithmanomaly detection
0 likes · 7 min read
Metric Logic Tree: Automated Anomaly Analysis for Business Metrics
Ctrip Technology
Ctrip Technology
Nov 30, 2017 · Information Security

Machine Learning Practices for Web Attack Detection in Ctrip's Nile System

This article describes how Ctrip's security team replaced rule‑based web attack detection with a Spark‑powered machine‑learning pipeline, detailing the system architecture, feature engineering using TF‑IDF, model training, evaluation, online deployment, and future enhancements to improve detection accuracy and performance.

PythonWeb Securityanomaly detection
0 likes · 17 min read
Machine Learning Practices for Web Attack Detection in Ctrip's Nile System
Efficient Ops
Efficient Ops
Nov 23, 2017 · Artificial Intelligence

How to Turn AIOps from Hype into Reality: A Practical Roadmap

In this comprehensive talk, Pei Dan outlines the technical and strategic roadmap for bringing AIOps to production, explains the challenges of anomaly detection, fault localization, root‑cause analysis and prediction, and demonstrates how to decompose complex operations problems into AI‑solvable tasks.

AIAIOpsanomaly detection
0 likes · 21 min read
How to Turn AIOps from Hype into Reality: A Practical Roadmap
Architect
Architect
Feb 1, 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 identification, wine quality assessment, fraud detection, recommendation systems, and authorship analysis.

anomaly detectionclassificationdata mining
0 likes · 14 min read
An Introduction to Data Mining Algorithms and Their Real-World Applications