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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.
The JD advertising team proposes a CTR‑driven advertising image generation framework (CAIG) that leverages multimodal large language models, a novel reward model, and product‑centric preference optimization to produce ad images with superior click‑through performance, validated by extensive offline and online experiments.
This article describes a lightweight, non‑intrusive MyBatis plugin that adds identifiable coloring comments to SQL statements—embedding statementId, pFinderId, and optional custom data—to simplify SQL source tracing, improve slow‑SQL analysis, and support SELECT, INSERT, UPDATE, DELETE operations with minimal performance overhead.
This article details JD Retail's AI engine that seamlessly supports both GPU and domestic NPU hardware, describing its heterogeneous cluster architecture, unified training and inference APIs, performance optimizations, extensive model coverage, and multiple production use cases across e‑commerce, logistics, and intelligent assistance.
This article provides a comprehensive overview of map trajectory technology, covering geographic coordinate systems, map projections, GIS software basics, data formats, GPS data processing, real‑time and historical trajectory analysis, and recent advances such as AI‑driven services and cross‑domain integrations.
This article details how JD Logistics guarantees the stability of its Double 11 supply‑chain dashboard by mapping the entire data‑flow, identifying risk points across ingestion, processing, storage, service, and monitoring layers, and applying targeted technical and organizational safeguards.
The article recounts a JD retail engineer’s rapid growth through tackling low‑resource large‑model training, developing a margin‑based dynamic data selection method (DynaMS) that earned an ICLR paper, and sharing practical insights on aligning business needs with cutting‑edge AI research.
This article explains the importance of business monitoring, distinguishes technical and business metrics, outlines a step‑by‑step process for constructing a business indicator system, and provides practical methods, tools, and common pitfalls for effective operations monitoring.
This article explains how Caffeine implements element expiration using three built‑in policies and a custom TimeWheel strategy, detailing the construction of the timer wheel, bucket selection, scheduling, advancing, and eviction logic within the cache's maintenance cycle.
This article provides a comprehensive walkthrough of Caffeine cache's architecture, explaining its fixed-size eviction policy, underlying data structures such as ConcurrentHashMap, MPSC buffers, Count‑Min Sketch frequency tracking, and the dynamic window‑probation‑protected zones, while detailing key methods like put, getIfPresent, and maintenance.
