Sep 1, 2022 · Databases

AI-Powered Database Anomaly Detection Service: Feature Analysis, Algorithm Selection, and Real-Time Monitoring

The article details Meituan's database platform team's end‑to‑end design of an AI‑driven anomaly detection service, covering feature analysis of time‑series patterns, algorithm selection (MAD, boxplot, EVT), model training, real‑time detection with Flink, operational metrics, and future enhancements.

AI AlgorithmsBoxplotDatabase Anomaly Detection

0 likes · 19 min read

AI-Powered Database Anomaly Detection Service: Feature Analysis, Algorithm Selection, and Real-Time Monitoring

Architects Research Society

Nov 21, 2016 · Artificial Intelligence

Data Science Q&A: Overfitting, Experimental Design, Tall/Wide Data, Chart Junk, Outliers, Extreme Value Theory, Recommendation Engines, and Visualization

This article presents a series of data‑science questions and expert answers covering overfitting, experimental design for user behavior, the distinction between tall and wide data, detecting chart junk, outlier detection methods, extreme‑value theory for rare events, recommendation‑engine fundamentals, and techniques for visualizing high‑dimensional data.

Extreme Value TheoryRecommendation Systemschart junk

0 likes · 18 min read

Data Science Q&A: Overfitting, Experimental Design, Tall/Wide Data, Chart Junk, Outliers, Extreme Value Theory, Recommendation Engines, and Visualization