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Lao Guo's Learning Space
Lao Guo's Learning Space
May 9, 2026 · Artificial Intelligence

How Top Credit Data Firms Use AI to Transform Risk Management: 5 Key Practices

AI is transforming credit risk assessment by automating data profiling, anomaly detection, rating, early warning, and compliance auditing, cutting manual review costs from millions, boosting data coverage to over 99%, improving consistency and speed, and enabling firms to shift from reactive to proactive risk control.

AICompliance Automationanomaly detection
0 likes · 11 min read
How Top Credit Data Firms Use AI to Transform Risk Management: 5 Key Practices
AI Engineer Programming
AI Engineer Programming
Apr 29, 2026 · Information Security

Managing AI Agents Like Engineering Teams: A Five‑Layer Governance Stack

The article presents a five‑layer governance stack for AI agents—identity, centralized tool registry, policy enforcement, behavioral anomaly detection, and unified security posture—detailing how each layer mirrors traditional engineering team management to reduce attack surface, audit complexity, and migration costs.

AI Agentsanomaly detectioncloud security
0 likes · 11 min read
Managing AI Agents Like Engineering Teams: A Five‑Layer Governance Stack
dbaplus Community
dbaplus Community
Apr 6, 2026 · Operations

How Machine Learning Transforms Database Monitoring: From Fixed Thresholds to Intelligent Anomaly Detection

This article explains why traditional threshold‑based database inspections are insufficient, introduces machine‑learning‑driven anomaly detection as a second set of eyes, details feature extraction, algorithm choices, tuning, and alert convergence, and showcases three real‑world scenarios with MySQL and Redis metrics.

DBADatabase MonitoringOperations
0 likes · 23 min read
How Machine Learning Transforms Database Monitoring: From Fixed Thresholds to Intelligent Anomaly Detection
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 26, 2026 · Artificial Intelligence

UniOD: A Single Model for Zero‑Training Cross‑Domain Anomaly Detection

UniOD introduces a universal outlier detection model that leverages historical labeled datasets to train one deep graph‑neural‑network‑based model, enabling plug‑and‑play anomaly detection on unseen domains without any retraining, and is backed by theoretical guarantees and extensive cross‑domain experiments.

Graph Neural NetworkUniODanomaly detection
0 likes · 10 min read
UniOD: A Single Model for Zero‑Training Cross‑Domain Anomaly Detection
Architecture Musings
Architecture Musings
Mar 25, 2026 · Information Security

Seeing AI Agent Drift in Vector Space: An Unvalidated Thought Experiment

The article imagines an AI coding agent that silently exfiltrates credentials hidden in data, explains why rule‑based and text‑level defenses miss such attacks, proposes monitoring the agent's vector‑space decision trajectory with six geometric metrics, and critically evaluates the feasibility and limitations of this approach.

AI AgentsLLMSecurity
0 likes · 23 min read
Seeing AI Agent Drift in Vector Space: An Unvalidated Thought Experiment
Advanced AI Application Practice
Advanced AI Application Practice
Mar 14, 2026 · Artificial Intelligence

How AI + Fiddler Transforms Software Testing

By training an AI model on normal network traffic, testers can let Fiddler automatically highlight security leaks, API errors, and performance degradation, turning a tedious manual review into a fast, reliable, and intelligent quality‑assurance process.

AIAutomationFiddler
0 likes · 5 min read
How AI + Fiddler Transforms Software Testing
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Mar 3, 2026 · Artificial Intelligence

How HORAI Uses Large‑Scale Multimodal Pretraining to Boost Time‑Series Forecasting and Anomaly Detection

The article reviews the HORAI model, which introduces a frequency‑enhanced multimodal pretraining paradigm and the massive MM‑TS dataset, showing that integrating derived images, endogenous text, and real‑world news dramatically improves zero‑shot forecasting and anomaly detection across six domains.

HORAIMultimodal LearningTime Series
0 likes · 23 min read
How HORAI Uses Large‑Scale Multimodal Pretraining to Boost Time‑Series Forecasting and Anomaly Detection
DeepHub IMBA
DeepHub IMBA
Mar 1, 2026 · Artificial Intelligence

Demystifying VAE: From Probabilistic Encoding to Latent Space Regularization

This article walks through the fundamentals of variational autoencoders, explaining why they are needed, detailing their three core components, loss formulation, PyTorch implementation, training loop, and multiple inference modes such as anomaly detection, data generation, conditional generation, latent space manipulation, and data imputation.

Conditional VAEGenerative ModelsLatent Space
0 likes · 15 min read
Demystifying VAE: From Probabilistic Encoding to Latent Space Regularization
AI Algorithm Path
AI Algorithm Path
Feb 19, 2026 · Artificial Intelligence

A Practical Guide to Industrial Defect Detection with Pre‑trained Neural Networks

The article explains how manufacturers can shift from defect‑specific vision models to anomaly detection by leveraging pre‑trained object‑detection networks, visualising feature maps, and applying memory‑bank methods such as PaDiM and PatchCore, with the open‑source Anomalib library as a ready‑to‑use solution.

AnomalibPaDiMPatchCore
0 likes · 7 min read
A Practical Guide to Industrial Defect Detection with Pre‑trained Neural Networks
AI Algorithm Path
AI Algorithm Path
Feb 18, 2026 · Artificial Intelligence

Using Autoencoders for Industrial Defect Detection

This article explains how to train a simple fully‑connected autoencoder on defect‑free images, use reconstruction error to highlight anomalies in industrial parts, and convert the error into a single metric that cleanly separates good from defective components.

AutoencoderComputer VisionKeras
0 likes · 7 min read
Using Autoencoders for Industrial Defect Detection
Raymond Ops
Raymond Ops
Jan 28, 2026 · Artificial Intelligence

From Alert Storms to Smart Ops: Unlocking AIOps for Modern IT Operations

This guide walks through the evolution from noisy alert storms to intelligent AIOps, covering AIOps fundamentals, why it matters now, core capabilities like anomaly detection, root‑cause analysis, capacity forecasting and self‑healing, a practical implementation roadmap, toolchain suggestions, common pitfalls, and future trends.

Capacity PredictionRoot Cause Analysisaiops
0 likes · 22 min read
From Alert Storms to Smart Ops: Unlocking AIOps for Modern IT Operations
DeWu Technology
DeWu Technology
Jan 7, 2026 · Operations

From Chaos to Clarity: Building Full‑Stack Observability for Poizon’s Algorithm Ecosystem

This article details how Poizon’s algorithm platform evolved from fragmented tracing to a unified, scenario‑driven observability system that standardizes traces, metrics, logs, and events, introduces a knowledge‑graph of algorithm scenes, and applies compression, async reporting, and advanced anomaly detection to improve stability and debugging efficiency.

Algorithm PlatformDistributed TracingLog Standardization
0 likes · 26 min read
From Chaos to Clarity: Building Full‑Stack Observability for Poizon’s Algorithm Ecosystem
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Nov 8, 2025 · Artificial Intelligence

Time-Series Paper Digest: Nov 1‑7 2025 Highlights

This digest summarizes three recent AI papers—DoFlow, Forecast2Anomaly, and ForecastGAN—detailing their causal generative flow model for interventions, a retrieval‑augmented framework for zero‑shot anomaly prediction, and a decomposition‑based adversarial approach that improves multi‑horizon forecasting across diverse datasets.

Deep LearningTime Seriesanomaly detection
0 likes · 8 min read
Time-Series Paper Digest: Nov 1‑7 2025 Highlights
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 29, 2025 · Artificial Intelligence

How AI Powers Proactive Risk Detection in Massive Cloud Platforms

This article outlines Alibaba Cloud's AI‑driven "Smart Sentinel" system, which tackles the three major challenges of large‑scale cloud operations—hard‑to‑detect anomalies, alarm storms, and difficult root‑cause analysis—by deploying multi‑layered detection, intelligent alarm grading, and an end‑to‑end automated response loop.

anomaly detectioncloud computingintelligent monitoring
0 likes · 11 min read
How AI Powers Proactive Risk Detection in Massive Cloud Platforms
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 25, 2025 · Artificial Intelligence

Time Series Paper Digest: Extreme Event Prediction, Multimodal Fusion & Anomaly Detection

This article summarizes four recent arXiv papers on time‑series forecasting, covering a hierarchical knowledge‑distillation framework for extreme events, a graph‑enhanced multimodal fusion network, an interpretable unsupervised anomaly detector, and an adaptive masking loss that improves prediction accuracy.

Time Seriesadaptive maskinganomaly detection
0 likes · 10 min read
Time Series Paper Digest: Extreme Event Prediction, Multimodal Fusion & Anomaly Detection
Qunar Tech Salon
Qunar Tech Salon
Oct 20, 2025 · Databases

Why Traditional DB Inspections Fail and AI-Powered Anomaly Detection Helps

This article examines the limitations of traditional threshold‑based database inspections, introduces AI‑driven anomaly detection techniques such as DoubleRollingAggregate, SeasonalAD, and LevelShiftAD, and details practical implementations, tuning strategies, and real‑world use cases for MySQL and Redis monitoring.

Database Monitoringanomaly detectionmachine learning
0 likes · 23 min read
Why Traditional DB Inspections Fail and AI-Powered Anomaly Detection Helps
Data Party THU
Data Party THU
Oct 16, 2025 · Fundamentals

Mastering Anomaly vs Novelty Detection with Distribution Fitting in Python

This article explains the fundamental differences between anomaly and novelty detection, outlines how to model univariate outliers using probability distribution fitting with the distfit library, and demonstrates the workflow on synthetic height data and real natural‑gas price data, including model selection, visualization, and prediction.

PythonStatistical Modelinganomaly detection
0 likes · 17 min read
Mastering Anomaly vs Novelty Detection with Distribution Fitting in Python
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 11, 2025 · Artificial Intelligence

Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)

This article summarizes eight newly released AI papers on multivariate time‑series forecasting and anomaly detection, detailing each work's motivation, proposed methodology, key innovations such as CRIB, TS‑JEPA, DSAT‑HD, DIMIGNN, ASTGI, IndexNet, TsLLM, Moon, TimeSeriesScientist, MLG‑4TS, and Augur, and reports their experimental validation on real‑world datasets.

Deep LearningTransformeranomaly detection
0 likes · 23 min read
Recent Advances in Multivariate Time Series Forecasting: Paper Summaries (Sep 27 – Oct 10 2025)
MaGe Linux Operations
MaGe Linux Operations
Sep 12, 2025 · Operations

From Alert Storms to Intelligent Ops: A Practical AIOps Journey

This article explores how AIOps transforms traditional IT operations by using AI for anomaly detection, root‑cause analysis, capacity forecasting, and self‑healing, offering a step‑by‑step roadmap, real‑world code examples, toolchain recommendations, common pitfalls, and future trends for building intelligent, automated operations.

Root Cause Analysisaiopsanomaly detection
0 likes · 24 min read
From Alert Storms to Intelligent Ops: A Practical AIOps Journey
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Sep 6, 2025 · Artificial Intelligence

Time Series Paper Digest (Aug 23–Sep 5 2025)

It presents concise summaries of six recent arXiv papers on unsupervised domain adaptation, efficient forecasting, SHAP explanations, text‑reinforced multimodal forecasting, online prediction with feature adjustment, zero‑shot forecasting zoo, and a new anomaly‑detection metric, highlighting methods, datasets, and results.

Multimodal LearningOnline LearningSHAP
0 likes · 16 min read
Time Series Paper Digest (Aug 23–Sep 5 2025)
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 5, 2025 · Operations

How Alibaba Scales Anomaly Detection Across Millions of Metrics

This article explains how Alibaba tackles anomaly detection for tens of millions of metrics in a 100‑thousand‑machine cluster by comparing vertical time‑series methods with horizontal clustering, choosing DBSCAN for large‑scale monitoring, and detailing the ETL, computation, and visualization pipeline.

DBSCANTime Seriesanomaly detection
0 likes · 6 min read
How Alibaba Scales Anomaly Detection Across Millions of Metrics
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 4, 2025 · Artificial Intelligence

Why Most Anomaly Detection Alerts Miss the Mark: The Hidden Bayesian Truth

This article explores how modern monitoring at Alibaba integrates data science and machine learning to define anomalies, reviews major detection techniques, examines challenges like seasonality, heteroscedasticity and complex cycles, evaluates detection performance with ROC analysis, and reveals why alerts often have only a 2% chance of indicating a true anomaly.

anomaly detectionbayesian probabilityfalse positives
0 likes · 12 min read
Why Most Anomaly Detection Alerts Miss the Mark: The Hidden Bayesian Truth
37 Interactive Technology Team
37 Interactive Technology Team
Jul 4, 2025 · Operations

How Dynamic Thresholds with Prophet Transform Monitoring from Static Alerts to Intelligent Insights

Traditional fixed‑threshold monitoring often triggers noisy alerts during routine business rhythms, but by modeling time‑series patterns with Facebook Prophet to predict dynamic confidence intervals, teams can automatically adjust thresholds, reduce false positives, and accurately detect true anomalies across diverse services.

ProphetTime Seriesanomaly detection
0 likes · 7 min read
How Dynamic Thresholds with Prophet Transform Monitoring from Static Alerts to Intelligent Insights
AIWalker
AIWalker
May 14, 2025 · Artificial Intelligence

How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters

This paper presents HGO‑YOLO, a lightweight real‑time anomaly‑behavior detector that integrates HGNetv2 and GhostConv into YOLOv8, achieving 87.4% mAP with just 4.6 MB of parameters and 56 FPS on CPU, and validates its performance across multiple datasets and hardware platforms.

Computer VisionLightweight ModelsYOLO
0 likes · 25 min read
How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters
php Courses
php Courses
May 12, 2025 · Artificial Intelligence

Anomaly Detection and Outlier Handling Using PHP and Machine Learning

This article explains how to detect and handle outliers in datasets using PHP and machine-learning techniques, covering the statistical Z-Score method and the Isolation Forest algorithm, and providing code examples for both removal and replacement of anomalous values to improve data quality and model accuracy.

Isolation ForestPHPanomaly detection
0 likes · 6 min read
Anomaly Detection and Outlier Handling Using PHP and Machine Learning
JD Tech
JD Tech
Apr 1, 2025 · Artificial Intelligence

Self‑Isolation Mechanism for Time‑Series Anomaly Detection with Memory Space

This article presents a self‑isolation based streaming anomaly detection framework that combines memory‑space indexing to capture pattern anomalies, long‑term memory, and concept drift in time‑series data, and validates the approach with public benchmarks and real‑world risk‑control scenarios.

Time Seriesanomaly detectionconcept drift
0 likes · 24 min read
Self‑Isolation Mechanism for Time‑Series Anomaly Detection with Memory Space
JD Retail Technology
JD Retail Technology
Mar 11, 2025 · Artificial Intelligence

Can Self‑Isolation Streams Detect Anomalies Faster? A Deep Dive into Time‑Series Anomaly Detection

This article presents a comprehensive analysis of a self‑isolation‑based streaming anomaly detection framework, covering business motivations, existing techniques, technical challenges such as pattern anomalies, long‑term memory and concept drift, the core self‑isolation mechanism, memory‑space architecture, experimental evaluations, and practical risk‑control applications.

Time Seriesanomaly detectionconcept drift
0 likes · 28 min read
Can Self‑Isolation Streams Detect Anomalies Faster? A Deep Dive into Time‑Series Anomaly Detection
JD Tech Talk
JD Tech Talk
Feb 27, 2025 · Artificial Intelligence

Can Self‑Isolation Streams Detect Real‑Time Anomaly Patterns?

This article presents a comprehensive study of streaming‑time‑series anomaly detection, introducing a self‑isolation mechanism combined with a memory space to capture pattern anomalies, handle concept drift, and reduce false alarms, supported by extensive experiments on public datasets and real‑world risk‑control scenarios.

Time Seriesanomaly detectionconcept drift
0 likes · 27 min read
Can Self‑Isolation Streams Detect Real‑Time Anomaly Patterns?
JD Cloud Developers
JD Cloud Developers
Feb 27, 2025 · Artificial Intelligence

Self-Isolation Streams: Boosting Real-Time Anomaly Detection for Time-Series

This article presents a comprehensive study of time‑series anomaly detection using a self‑isolation mechanism combined with a memory‑space architecture, addressing pattern anomalies, long‑term memory, and concept drift, and demonstrates its effectiveness through extensive experiments and real‑world risk‑control scenarios.

Time Seriesanomaly detectionconcept drift
0 likes · 28 min read
Self-Isolation Streams: Boosting Real-Time Anomaly Detection for Time-Series
Huolala Tech
Huolala Tech
Feb 18, 2025 · Information Security

How to Detect and Mitigate API Anomalies Using Traffic Analysis and ML

This article outlines a practical approach to API anomaly detection, covering background, objectives, a comprehensive framework, feature engineering, threshold profiling, daily operations, detection methods, anomaly types, and response strategies, all driven by big‑data and machine‑learning techniques.

Traffic analysisanomaly detectionreal-time monitoring
0 likes · 10 min read
How to Detect and Mitigate API Anomalies Using Traffic Analysis and ML
php Courses
php Courses
Feb 5, 2025 · Artificial Intelligence

Anomaly Detection and Outlier Handling in PHP Using Machine Learning

This article explains how to detect and handle outliers in data sets using PHP and machine learning techniques, covering statistical Z‑Score detection, Isolation Forest algorithm, and practical code examples for removing or replacing anomalous values to improve data quality and model accuracy.

Isolation ForestOutlier HandlingPHP
0 likes · 6 min read
Anomaly Detection and Outlier Handling in PHP Using Machine Learning
Huolala Safety Emergency Response Center
Huolala Safety Emergency Response Center
Jan 9, 2025 · Information Security

Detecting API Anomalous Traffic with Big Data and Machine Learning

This article outlines a comprehensive approach to API anomaly detection, covering background, objectives, a two‑layer framework with offline and real‑time feature pipelines, threshold profiling, detection methods, strategy types, and operational practices to mitigate data leakage and abuse.

Big DataReal-time ProcessingThreshold Modeling
0 likes · 10 min read
Detecting API Anomalous Traffic with Big Data and Machine Learning
Baidu Geek Talk
Baidu Geek Talk
Dec 18, 2024 · Artificial Intelligence

GEE Graph Embedding Algorithm for Business Security Anomaly Detection

The article presents the GEE (Graph Encoder Embedding) algorithm for business security anomaly detection, explains its label‑propagation foundation, evaluates it on ten‑million‑edge real data, identifies inefficiencies in the original implementation, and demonstrates that vectorized NumPy/Pandas optimizations reduce runtime from 55 seconds to about 4 seconds while preserving meaningful TSNE‑visualized embeddings.

GEE algorithmanomaly detectionanti-fraud
0 likes · 21 min read
GEE Graph Embedding Algorithm for Business Security Anomaly Detection
dbaplus Community
dbaplus Community
Dec 16, 2024 · Operations

How Qunar Built a 5‑Million‑Metric Radar System to Cut Ticket Failures by 87%

This article details the design, implementation, and results of Qunar's intelligent ticket‑monitoring Radar system, covering the business need, architecture, anomaly‑detection algorithms, test‑set construction, parameter tuning, and the achieved 87% detection accuracy with future plans for large‑model integration.

OperationsReliabilityanomaly detection
0 likes · 17 min read
How Qunar Built a 5‑Million‑Metric Radar System to Cut Ticket Failures by 87%
php Courses
php Courses
Dec 2, 2024 · Artificial Intelligence

Anomaly Detection and Outlier Handling in PHP Using Z-Score and Isolation Forest

This article explains how to detect and handle outliers in data using PHP, covering statistical Z-Score and Isolation Forest methods, and provides sample code for both detection and subsequent removal or replacement of anomalous values to improve data quality and model accuracy.

Isolation ForestOutlier HandlingPHP
0 likes · 7 min read
Anomaly Detection and Outlier Handling in PHP Using Z-Score and Isolation Forest
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Oct 9, 2024 · Operations

AIOps Implementation at Xiaohongshu: Fault Localization and Intelligent Operations

Xiaohongshu’s AIOps initiative builds a four‑layer framework that leverages machine‑learning‑driven anomaly detection, causal analysis, and trace‑based fault localization to automatically identify root‑cause services in micro‑service environments, achieving over 80 % accuracy across 1000 daily diagnoses while guiding future enhancements in change correlation and automated remediation.

DevOpsFault LocalizationIntelligent Operations
0 likes · 28 min read
AIOps Implementation at Xiaohongshu: Fault Localization and Intelligent Operations
php Courses
php Courses
Sep 4, 2024 · Artificial Intelligence

Anomaly Detection and Outlier Handling Using PHP and Machine Learning

This article explains how to detect and handle outliers in data using PHP, covering statistical Z-Score detection and the Isolation Forest algorithm, and provides sample code for both detection and subsequent removal or replacement of anomalous values to improve data quality.

Isolation ForestOutlier Handlinganomaly detection
0 likes · 6 min read
Anomaly Detection and Outlier Handling Using PHP and Machine Learning
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 27, 2024 · Artificial Intelligence

How AI Detects Cluster-Wide Task Slowdowns in Cloud Systems

A new AI‑driven method for detecting cluster‑wide task slowdowns in cloud platforms improves F1 score by 5.3% over state‑of‑the‑art techniques, addressing challenges of composite periodic patterns, training data contamination, and focusing on slowdown anomalies.

Neural NetworksTime SeriesUnsupervised Learning
0 likes · 8 min read
How AI Detects Cluster-Wide Task Slowdowns in Cloud Systems
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Aug 26, 2024 · Cloud Computing

How Neural Attention Detects Cluster-Wide Task Slowdowns in Cloud Systems

A new paper accepted at ACM SIGKDD2024 presents a neural‑network‑based framework that uses a skim‑attention mechanism and a picky loss function to accurately detect cluster‑wide task slowdown anomalies in large‑scale cloud platforms, achieving a 5.3% average F1‑score improvement over state‑of‑the‑art methods.

Cluster PerformanceNeural Networksanomaly detection
0 likes · 5 min read
How Neural Attention Detects Cluster-Wide Task Slowdowns in Cloud Systems
Baidu Geek Talk
Baidu Geek Talk
Aug 7, 2024 · Artificial Intelligence

Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach

This article presents an embedding‑based method for time‑series anomaly detection in security and anti‑cheat scenarios, explains how to vectorise logs, sample and compute distribution features, details implementation code, and validates the approach with four synthetic experiments showing precision‑recall improvements at day and hour granularity.

EmbeddingSecurityTime Series
0 likes · 12 min read
Detecting Time‑Series Anomalies in Embedding Space: A Practical AI Approach
DeWu Technology
DeWu Technology
Jul 19, 2024 · Artificial Intelligence

AI‑Powered Anomaly Detection Algorithms for Observability Metrics

The article explains how AI‑powered anomaly detection—using statistical 3‑sigma/Z-score methods, unsupervised machine‑learning like Isolation Forest, and deep‑learning models such as LSTM, Transformer and Pyraformer—overcomes the limits of threshold‑based monitoring by preprocessing data, reducing false alerts, and delivering high‑precision observability metrics.

AIDeep Learninganomaly detection
0 likes · 13 min read
AI‑Powered Anomaly Detection Algorithms for Observability Metrics
DataFunTalk
DataFunTalk
Jul 14, 2024 · Artificial Intelligence

Time Series and Machine Learning – An Overview and Book Introduction

The article introduces the rapid rise of large language models, the abundance of time‑series data in many sectors, and explains how combining machine‑learning and deep‑learning techniques with time‑series analysis has become a research hotspot, culminating in a new book that systematically covers theory, methods, and real‑world applications.

AIanomaly detectionmachine learning
0 likes · 10 min read
Time Series and Machine Learning – An Overview and Book Introduction
Qunar Tech Salon
Qunar Tech Salon
Jun 12, 2024 · Artificial Intelligence

Design and Implementation of Qunar Flight Ticket Intelligent Alert (Radar) System

This article presents a comprehensive analysis and engineering of Qunar's flight‑ticket intelligent pre‑warning (Radar) system, covering the business need, value analysis, architectural redesign, feature extraction, indicator classification, accuracy quantification, multi‑algorithm anomaly detection, automatic parameter tuning, observed effects, and future plans to incorporate large‑model techniques.

Operationsanomaly detectionflight ticket
0 likes · 17 min read
Design and Implementation of Qunar Flight Ticket Intelligent Alert (Radar) System
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 5, 2024 · Artificial Intelligence

LARA: A Light, Anti‑Overfitting Retraining Method for Unsupervised Time‑Series Anomaly Detection

The LARA approach, presented at WWW2024, offers a lightweight, anti‑overfitting retraining solution for unsupervised time‑series anomaly detection in cloud services, achieving state‑of‑the‑art accuracy with minimal new data and dramatically reducing training overhead.

Cloud AITime SeriesUnsupervised Learning
0 likes · 5 min read
LARA: A Light, Anti‑Overfitting Retraining Method for Unsupervised Time‑Series Anomaly Detection
Python Programming Learning Circle
Python Programming Learning Circle
May 10, 2024 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Code Examples

This article compiles and explains a variety of common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing algorithm descriptions, workflow steps, advantages, limitations, and ready‑to‑run Python code snippets for each approach.

PythonUnsupervised Learninganomaly detection
0 likes · 23 min read
Comprehensive Overview of Common Anomaly Detection Methods with Code Examples
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
May 7, 2024 · Artificial Intelligence

How Alibaba’s New Time‑Series Models Are Redefining Forecasting and Anomaly Detection

Alibaba Cloud’s big‑data research team announced four groundbreaking time‑series papers accepted at ICLR 2024, ICDE 2024 and WWW 2024, introducing models such as Pathformer, ContraLSP, MACE, and LARA that advance multi‑scale forecasting, explainable AI, and efficient anomaly detection for intelligent operations.

AITime Seriesanomaly detection
0 likes · 11 min read
How Alibaba’s New Time‑Series Models Are Redefining Forecasting and Anomaly Detection
DataFunSummit
DataFunSummit
Apr 6, 2024 · Information Security

Comprehensive Guide to Malicious Website Anti‑Fraud: Detection, Operation, and Modeling

This article provides a detailed overview of malicious website anti‑fraud, covering classification, development, operational tactics, revenue models, multi‑dimensional anomaly detection, and advanced counter‑measure models such as fingerprint, text, image, complex network, and multimodal approaches.

Graph Neural Networkanomaly detectionanti-fraud
0 likes · 16 min read
Comprehensive Guide to Malicious Website Anti‑Fraud: Detection, Operation, and Modeling
Efficient Ops
Efficient Ops
Mar 31, 2024 · Operations

Why Most Alerts Fail and How to Design Actionable Monitoring

Most system alerts are poorly designed, flooding engineers with noise; this article explains the essence of alerts, distinguishes business rule vs reliability monitoring, outlines effective metrics and strategies, and presents simple anomaly-detection algorithms to create actionable, high-quality alerts.

alert designanomaly detectionmonitoring
0 likes · 21 min read
Why Most Alerts Fail and How to Design Actionable Monitoring
DataFunTalk
DataFunTalk
Mar 11, 2024 · Artificial Intelligence

Anomaly Detection and Attribution Diagnosis Practices at Ant Financial

This article presents Ant Financial's practical approaches to anomaly detection and attribution diagnosis, detailing the underlying concepts, four methodological categories, specific algorithms such as VBEM, AnoSVGD and Autoformer, multi‑dimensional factor analysis, real‑world challenges, and operational benefits for KPI monitoring and incident response.

AIAttribution Analysisanomaly detection
0 likes · 13 min read
Anomaly Detection and Attribution Diagnosis Practices at Ant Financial
php Courses
php Courses
Mar 5, 2024 · Artificial Intelligence

Anomaly Detection and Outlier Handling in PHP Using Machine Learning

This article explains how to detect and handle outliers in datasets using PHP and machine‑learning techniques, covering Z‑Score and Isolation Forest algorithms as well as methods to delete or replace anomalous values to improve data quality and model accuracy.

Isolation ForestPHPanomaly detection
0 likes · 5 min read
Anomaly Detection and Outlier Handling in PHP Using Machine Learning
dbaplus Community
dbaplus Community
Jan 29, 2024 · Artificial Intelligence

How Meituan Uses AIOps to Revolutionize Incident Management

This article details Meituan's two‑year exploration of AIOps for incident management, covering the challenges of massive, real‑time operational data, the AI‑driven modules for risk prevention, fault detection, diagnosis, and similar‑incident recommendation, and future directions such as intelligent log detection and change recognition.

OperationsRoot Cause Analysisaiops
0 likes · 22 min read
How Meituan Uses AIOps to Revolutionize Incident Management
Tencent Cloud Developer
Tencent Cloud Developer
Jan 23, 2024 · Information Security

Metis: Understanding and Enhancing In-Network Regular Expressions

Metis combines deterministic finite automata conversion, byte‑level RNN training, and knowledge‑distilled random‑forest models to replace traditional regex matching on resource‑constrained network devices, delivering comparable accuracy while achieving up to 74× higher throughput and significant resource savings in DDoS protection and P4 forwarding.

In‑network computingNeurIPS 2023P4 Programmable Switches
0 likes · 9 min read
Metis: Understanding and Enhancing In-Network Regular Expressions
High Availability Architecture
High Availability Architecture
Jan 9, 2024 · Operations

AIOps Practices for Incident Management at Meituan: From Risk Prevention to Post‑Operation

This article presents Meituan's two‑year exploration of AIOps in incident management, detailing risk‑prevention change detection, real‑time anomaly discovery, automated root‑cause diagnosis, multi‑dimensional KPI analysis, and similar‑event recommendation, while sharing architectural designs, algorithmic techniques, performance results, and future directions.

NLPOperationsRoot Cause Analysis
0 likes · 24 min read
AIOps Practices for Incident Management at Meituan: From Risk Prevention to Post‑Operation
58 Tech
58 Tech
Dec 22, 2023 · Big Data

Design and Implementation of the JiShi BI Data Visualization Platform

This article details the architecture, core processes, and module designs of the JiShi BI platform, a self‑built data visualization and analysis system that integrates data ingestion, processing, enhanced AI‑driven analytics, and multi‑dimensional dashboard capabilities to support enterprise decision‑making.

BIData PlatformData visualization
0 likes · 12 min read
Design and Implementation of the JiShi BI Data Visualization Platform
Meituan Technology Team
Meituan Technology Team
Dec 21, 2023 · Operations

AIOps for Incident Management: Practices and Insights from Meituan

Meituan’s service‑operations team applies AIOps across prevention, detection, and post‑incident stages—using change‑risk analysis, real‑time graph‑based anomaly detection, similarity‑driven root‑cause diagnosis, and NLP‑powered incident recommendation—to achieve sub‑second detection, high precision, 28% faster fault handling, and plans for intelligent log and change recognition.

OperationsRoot Cause Analysisaiops
0 likes · 24 min read
AIOps for Incident Management: Practices and Insights from Meituan
Ctrip Technology
Ctrip Technology
Oct 19, 2023 · Artificial Intelligence

Anomaly Detection and Root Cause Analysis System for Ctrip Train Ticket Business Metrics

This article presents an AI‑driven system that automatically detects anomalies in over 1,000 Ctrip train‑ticket business metrics using six unsupervised algorithms and locates their root causes through a hard‑voting ensemble of four specialized methods, demonstrating practical results and future enhancements.

CtripRoot Cause AnalysisTime Series
0 likes · 18 min read
Anomaly Detection and Root Cause Analysis System for Ctrip Train Ticket Business Metrics
DataFunSummit
DataFunSummit
Oct 10, 2023 · Big Data

Real-Time Risk Insight: Architecture Evolution and Future Outlook

This article presents a comprehensive overview of the challenges, architectural evolution from version 1.0 to 3.0, core components, key technologies, and future directions of JD's real‑time risk insight platform, highlighting data integration, streaming processing, plugin mechanisms, and intelligent anomaly detection.

anomaly detectionarchitecturedata pipeline
0 likes · 18 min read
Real-Time Risk Insight: Architecture Evolution and Future Outlook
360 Tech Engineering
360 Tech Engineering
Oct 8, 2023 · Fundamentals

Data Anomaly Analysis: Methods, Process, and Case Studies

This article systematically outlines the thinking, step‑by‑step process, and practical methods for identifying and diagnosing data anomalies, and illustrates the approach with three detailed case studies covering video playback spikes, app retention drops, and community conversion declines.

Business IntelligenceRoot Cause Analysisanomaly detection
0 likes · 16 min read
Data Anomaly Analysis: Methods, Process, and Case Studies
Test Development Learning Exchange
Test Development Learning Exchange
Sep 12, 2023 · Artificial Intelligence

Various Anomaly Detection Techniques with Python Code Examples

This article introduces ten common anomaly detection approaches—including statistical thresholds, boxplots, clustering, isolation forest, LOF, collaborative filtering, robust covariance, NLP, computer‑vision, and time‑series methods—each accompanied by concise Python code snippets illustrating how to identify outliers in different data domains.

PythonTime Seriesanomaly detection
0 likes · 9 min read
Various Anomaly Detection Techniques with Python Code Examples
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Aug 19, 2023 · Artificial Intelligence

Detecting Time‑Series Anomalies with the Anomaly Transformer’s Association Discrepancy

The article explains how the Anomaly Transformer leverages prior‑ and series‑association discrepancies, a learnable Gaussian kernel, and a Minimax training strategy to distinguish normal from abnormal points in time‑series data, achieving state‑of‑the‑art results on five benchmark datasets.

Association DiscrepancyMinimax TrainingSOTA
0 likes · 6 min read
Detecting Time‑Series Anomalies with the Anomaly Transformer’s Association Discrepancy
Ctrip Technology
Ctrip Technology
Aug 3, 2023 · Operations

Intelligent Anomaly Detection for Ctrip Operations: LSTM Forecasting, Trend Analysis, Adaptive Thresholds, and Periodic Anomaly Filtering

The article describes Ctrip's AIOps approach to improving alert quality by combining statistical methods and machine‑learning models such as LSTM, trend analysis, adaptive threshold calculation, and dynamic‑time‑warping based periodic anomaly detection, achieving significant gains in precision and fault‑recall rates.

LSTMTime Seriesadaptive threshold
0 likes · 12 min read
Intelligent Anomaly Detection for Ctrip Operations: LSTM Forecasting, Trend Analysis, Adaptive Thresholds, and Periodic Anomaly Filtering
Bitu Technology
Bitu Technology
Jul 7, 2023 · Artificial Intelligence

Building a KPI Alert System with Matrix Profiling and MACD at Tubi

An in‑depth case study describes how Tubi’s data‑science team built a flexible KPI alert system that tackles seasonal trends and diverse metric scales by applying seasonal decomposition, Matrix Profiling via the Stumpy library for anomaly detection, and MACD for trend analysis, achieving higher true‑positive rates while reducing false alarms.

KPI monitoringMACDTime Series
0 likes · 11 min read
Building a KPI Alert System with Matrix Profiling and MACD at Tubi
Meituan Technology Team
Meituan Technology Team
Jul 6, 2023 · Databases

Meituan Database Fault Detection, Diagnosis, and Kernel Observability Practices

The article explains the design of Meituan’s MySQL autonomous platform, detailing its four‑layer architecture, statistical dynamic‑threshold anomaly detection, model selection based on time‑series distribution, kernel‑level root‑cause analysis for replication lag, large‑transaction diagnostics, and crash investigation using core‑dump and signal analysis.

Autonomous PlatformKernel DiagnosticsLarge Transactions
0 likes · 29 min read
Meituan Database Fault Detection, Diagnosis, and Kernel Observability Practices
Ctrip Technology
Ctrip Technology
May 25, 2023 · Artificial Intelligence

Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control

This article presents a graph‑neural‑network driven, unsupervised approach that builds heterogeneous user‑feature graphs, learns node weights, constructs user‑user similarity graphs, and applies threshold‑based clustering to identify abnormal registration clusters for fraud detection in Ctrip's business travel platform.

Graph Neural NetworkUnsupervised Learninganomaly detection
0 likes · 12 min read
Graph-Based Unsupervised Model for Detecting Malicious Account Clusters in Registration Risk Control
DataFunTalk
DataFunTalk
May 10, 2023 · Artificial Intelligence

AI‑Driven Predictive Maintenance for NIO Power: GAN and Conceptor Techniques for PHM

This article presents NIO Power's intelligent equipment health management solution, detailing business background, operational challenges, PHM difficulties, and frontier AI technologies such as GAN‑based unsupervised anomaly detection and Conceptor‑based small‑sample fault diagnosis, illustrated with real‑world case studies and a comprehensive Q&A.

ConceptorGANNIO Power
0 likes · 28 min read
AI‑Driven Predictive Maintenance for NIO Power: GAN and Conceptor Techniques for PHM
Efficient Ops
Efficient Ops
Mar 14, 2023 · Artificial Intelligence

How NetEase Games Built an AIOps Platform to Transform IT Operations

This article explains how NetEase Games leveraged AI, big data, and machine learning to create an AIOps platform that automates anomaly detection, log analysis, and fault localization, improving quality assurance, cost management, and operational efficiency across complex gaming infrastructures.

IT Operationsaiopsanomaly detection
0 likes · 12 min read
How NetEase Games Built an AIOps Platform to Transform IT Operations
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Mar 10, 2023 · Operations

How Time-Series Decomposition Boosts Microservice Root Cause Localization to 84% Accuracy

This paper presents StudRank, a microservice root‑cause localization method that decomposes call‑chain traces into time‑series, detects anomalies, builds an abnormal propagation graph, and applies a personalized PageRank random‑walk algorithm, achieving 84% top‑1 accuracy and a 97.6% improvement over MicroRCA on public AIOps data.

MicroservicesStudRankaiops
0 likes · 23 min read
How Time-Series Decomposition Boosts Microservice Root Cause Localization to 84% Accuracy
Python Programming Learning Circle
Python Programming Learning Circle
Mar 6, 2023 · Operations

Intelligent Operations: AI‑Driven Anomaly Detection, Alarm Compression, and Log Analysis Techniques

This article presents an AI‑enhanced operations framework that combines metric anomaly detection, alarm compression, log anomaly detection, and intelligent analysis using machine learning methods such as DBSCAN clustering, SARIMAX modeling, Apriori association rules, and LSTM‑based log parsing to improve fault detection and reduce operational costs.

Operationsaiopsanomaly detection
0 likes · 15 min read
Intelligent Operations: AI‑Driven Anomaly Detection, Alarm Compression, and Log Analysis Techniques
vivo Internet Technology
vivo Internet Technology
Feb 15, 2023 · Information Security

Ad Traffic Anti‑Fraud: Algorithms, System Architecture, and Case Studies

The article explains how ad traffic fraud—ranging from simulated impressions to click farms—can be combated using a four‑layer risk‑control system that leverages unsupervised (DBSCAN, Isolation Forest) and supervised (Logistic Regression, Random Forest) algorithms, detailing data pipelines, model training, monitoring, and real‑world case studies.

Ad FraudAdvertisingRisk Detection
0 likes · 15 min read
Ad Traffic Anti‑Fraud: Algorithms, System Architecture, and Case Studies
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Nov 18, 2022 · Artificial Intelligence

Machine Learning-Based Anomaly Detection for Core Business Metrics

The paper proposes a containerized, machine‑learning framework that fuses rule‑based and XGBoost‑driven anomaly detection to monitor daily active users on a cloud music platform, achieving 89 % recall, 81 % precision and up to 74 % recall improvement over traditional threshold methods, while outlining future model refinement and broader metric applicability.

3-sigmaData IntelligenceHolt-Winters
0 likes · 11 min read
Machine Learning-Based Anomaly Detection for Core Business Metrics
JD Cloud Developers
JD Cloud Developers
Nov 7, 2022 · Artificial Intelligence

Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion

This article presents a threshold‑free anomaly detection framework for streaming time series that combines an LSTM‑based baseline module with an unsupervised detection module, detailing the architecture, training process, data preprocessing, and experimental results that demonstrate superior accuracy and F1 scores.

Deep LearningLSTMTime Series
0 likes · 15 min read
Detecting Time‑Series Anomalies Without Thresholds Using LSTM and Unsupervised Fusion
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Oct 11, 2022 · Artificial Intelligence

GANomaly: Theory and Source Code Analysis

This article explains the GANomaly model for semi‑supervised anomaly detection, detailing its generator‑encoder‑discriminator architecture, loss functions, testing phase scoring, and provides annotated PyTorch source code to help readers implement and understand the approach.

Deep LearningEncoder-DecoderGAN
0 likes · 15 min read
GANomaly: Theory and Source Code Analysis
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Sep 28, 2022 · Artificial Intelligence

EGBAD: Efficient GAN‑Based Anomaly Detection – Theory and Practical Implementation

This article introduces the EGBAD model, an efficient GAN‑based anomaly detection method that replaces AnoGAN's costly latent variable search with an encoder, provides detailed PyTorch code for data loading, model construction, training, and inference, and compares its testing speed with AnoGAN.

DiscriminatorEGBADEncoder
0 likes · 18 min read
EGBAD: Efficient GAN‑Based Anomaly Detection – Theory and Practical Implementation
NetEase Game Operations Platform
NetEase Game Operations Platform
Sep 19, 2022 · Artificial Intelligence

Applying AIOps to Game Operations: Roadmap, Anomaly Detection, and Fault Localization

This article describes NetEase's AIOps journey for game operations, explaining the Gartner definition of intelligent operations, the implementation roadmap, detailed anomaly‑detection techniques for business, performance, and log data, and a comprehensive fault‑localization workflow that combines resource, code, and historical analysis.

Fault Localizationaiopsanomaly detection
0 likes · 12 min read
Applying AIOps to Game Operations: Roadmap, Anomaly Detection, and Fault Localization
Model Perspective
Model Perspective
Aug 13, 2022 · Artificial Intelligence

Mastering Outlier Detection: Techniques, Algorithms, and PyOD Implementation

Outlier detection identifies data points far from the norm, using methods such as the 3‑sigma rule, boxplots, K‑Nearest Neighbors, and numerous probabilistic and proximity‑based algorithms, with practical PyOD code examples for training, evaluating, and visualizing models across various techniques.

anomaly detectionmachine learningoutlier detection
0 likes · 8 min read
Mastering Outlier Detection: Techniques, Algorithms, and PyOD Implementation
Cloud Native Technology Community
Cloud Native Technology Community
Aug 4, 2022 · Cloud Computing

Four Steps to Avoiding a Cloud Cost Incident

The article outlines four practical steps—establishing a robust tagging strategy, clarifying cost ownership, building and monitoring budgets, and implementing cost anomaly detection—to help enterprises accurately allocate, track, and control cloud spending, thereby preventing costly incidents and optimizing financial performance.

Cost Managementanomaly detectionbudgeting
0 likes · 6 min read
Four Steps to Avoiding a Cloud Cost Incident
Python Programming Learning Circle
Python Programming Learning Circle
Jul 15, 2022 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Python Code Examples

This article compiles and explains various common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing theoretical descriptions, algorithmic steps, advantages, limitations, and Python code examples for each approach.

Pythonanomaly detectionoutlier detection
0 likes · 18 min read
Comprehensive Overview of Common Anomaly Detection Methods with Python Code Examples
Efficient Ops
Efficient Ops
May 30, 2022 · Operations

How AIOps Transforms Enterprise Operations: Insights from China’s 2022 Tech Salon

The 2022 online AIOps Technology Salon hosted by the China Academy of Information and Communications Technology gathered over 13,000 viewers, featured expert talks on standards, practical implementations, anomaly‑detection algorithms, and real‑world case studies from major enterprises, offering actionable insights for modern IT operations.

EnterpriseIT OperationsTech Talk
0 likes · 4 min read
How AIOps Transforms Enterprise Operations: Insights from China’s 2022 Tech Salon
DataFunSummit
DataFunSummit
May 8, 2022 · Artificial Intelligence

Machine Learning‑Based Time Series Forecasting and Anomaly Detection System at JD Search

The article describes JD Search's machine‑learning alert system that combines offline and real‑time training, FFT‑based periodic detection, Prophet forecasting, and DBSCAN anomaly clustering, and explains architectural design, data preprocessing, model optimization, and distributed deployment to improve alert accuracy and response speed.

DBSCANFFTProphet
0 likes · 10 min read
Machine Learning‑Based Time Series Forecasting and Anomaly Detection System at JD Search
DataFunTalk
DataFunTalk
Apr 24, 2022 · Artificial Intelligence

Machine Learning‑Driven Time Series Forecasting and Anomaly Detection System at JD Search

The article describes JD Search’s machine‑learning‑based time‑series forecasting and anomaly‑detection platform, detailing its overall architecture, offline and real‑time training pipelines, FFT‑based periodicity detection, Prophet forecasting, DBSCAN outlier detection, and distributed optimizations such as Alink integration and load‑balancing strategies.

DBSCANFFTProphet
0 likes · 10 min read
Machine Learning‑Driven Time Series Forecasting and Anomaly Detection System at JD Search
IT Services Circle
IT Services Circle
Mar 23, 2022 · Artificial Intelligence

Local Outlier Factor (LOF) Algorithm: Theory, Workflow, Pros & Cons, and Python Implementation

This article introduces the classic density‑based anomaly detection method Local Outlier Factor (LOF), explains its underlying concepts such as k‑distance, reachability distance, and local reachability density, outlines the algorithm steps, discusses its advantages and limitations, and provides practical Python examples using PyOD and scikit‑learn.

LOFPythonanomaly detection
0 likes · 10 min read
Local Outlier Factor (LOF) Algorithm: Theory, Workflow, Pros & Cons, and Python Implementation
DataFunSummit
DataFunSummit
Mar 22, 2022 · Artificial Intelligence

Housing Price Estimation and Average Price Calculation Using 58.com Data and CatBoost

This article presents a comprehensive overview of 58.com’s real‑estate price system, describes how average prices are computed from platform data, explains three anomaly‑detection methods, and details a CatBoost‑based machine‑learning model for automated house valuation, including feature engineering and evaluation metrics.

CatBoostReal Estate Dataanomaly detection
0 likes · 15 min read
Housing Price Estimation and Average Price Calculation Using 58.com Data and CatBoost
JD Retail Technology
JD Retail Technology
Mar 7, 2022 · Artificial Intelligence

AI-Driven UI Testing: Data Collection, Model Development, and Deployment for Mobile App Anomaly Detection

This article presents a comprehensive study on applying AI and deep‑learning techniques to mobile UI testing, covering background challenges, feasibility research, abnormal sample construction, model design, training, evaluation, and future directions for intelligent test automation.

AI testingComputer VisionModel Training
0 likes · 13 min read
AI-Driven UI Testing: Data Collection, Model Development, and Deployment for Mobile App Anomaly Detection
DataFunSummit
DataFunSummit
Dec 9, 2021 · Big Data

Diagnostic Analytics in Meituan Food Delivery: Methods and Case Studies

This talk by Meituan data analyst Wang Qing explains why diagnostic analytics is essential, outlines its methodology using logical trees and hypothesis-driven approaches, and presents two case studies—weather index modeling and an intelligent anomaly detection system—to illustrate how data-driven diagnosis can pinpoint root causes and improve decision‑making in online food delivery.

Data ScienceRoot Cause Analysisanomaly detection
0 likes · 19 min read
Diagnostic Analytics in Meituan Food Delivery: Methods and Case Studies
Alibaba Cloud Native
Alibaba Cloud Native
Nov 3, 2021 · Operations

Unlocking Smart Anomaly Detection in Alibaba Cloud Prometheus

This article explains the fundamentals of time‑series anomaly detection, the limitations of static threshold rules in open‑source Prometheus, and how Alibaba Cloud Prometheus introduces template‑based and smart detection operators to handle spikes, periodic patterns, and data quality issues in AIOps scenarios.

Cloud NativePrometheusSmart Operator
0 likes · 11 min read
Unlocking Smart Anomaly Detection in Alibaba Cloud Prometheus
DataFunTalk
DataFunTalk
Sep 28, 2021 · Artificial Intelligence

Graph Modeling and GCN Exploration at 极验: Evolution, Offline and Real‑time Solutions

The talk presents an overview of graph neural network development, explains 极验's graph modeling research and evolution, and details offline and real‑time GCN solutions, including self‑supervised training, large‑scale handling, and performance comparisons, highlighting practical applications in fraud detection and risk control.

GCNGraph ModelingReal-time inference
0 likes · 26 min read
Graph Modeling and GCN Exploration at 极验: Evolution, Offline and Real‑time Solutions
DataFunTalk
DataFunTalk
Jul 23, 2021 · Artificial Intelligence

Ad Fraud Detection and Risk Control Practices at Alibaba Mama

This article explains Alibaba Mama's ad fraud risk control workflow, defines invalid traffic types, describes perception, insight, and disposal mechanisms, and outlines the AI‑driven detection models, evaluation metrics, and future research directions for large‑scale advertising security.

Ad FraudAlibabaanomaly detection
0 likes · 14 min read
Ad Fraud Detection and Risk Control Practices at Alibaba Mama
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.

Alibabaad fraud detectionanomaly 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.

Big DataCapacity PredictionIT Operations
0 likes · 13 min read
AIOps Overview: Concepts, Applications, and Case Studies
dbaplus Community
dbaplus Community
May 16, 2021 · Operations

How DBSCAN Clustering and Bayesian Inference Enable Fast Root‑Cause Detection in Securities Trading Systems

This article details the challenges of root‑cause identification in high‑availability securities trading platforms and presents two intelligent‑operations solutions—DBSCAN‑based clustering and Bayesian inference—to quickly locate anomalies and improve recovery efficiency.

Bayesian inferenceDBSCANIntelligent Operations
0 likes · 17 min read
How DBSCAN Clustering and Bayesian Inference Enable Fast Root‑Cause Detection in Securities Trading Systems
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.

Operationsanomaly detectionlarge-scale systems
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.

Big DataBoxplotJava
0 likes · 11 min read
Statistical Monitoring Using Normal Distribution and Boxplot: Theory, Implementation, and API Design