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JD Tech

Aug 22, 2024 · Backend Development

Designing a Disaster‑Recovery Data Backup System for JD’s LBS C‑End SOA Service

This article explores the design and implementation of a disaster‑recovery data‑backup architecture for JD’s LBS C‑end SOA service, covering backup strategies, cost‑reduction techniques, grid‑based indexing with H3, client‑side caching, diff verification, and deployment considerations to balance reliability, performance, and expense.

Cost OptimizationLBSSOA

0 likes · 18 min read

Designing a Disaster‑Recovery Data Backup System for JD’s LBS C‑End SOA Service

JD Retail Technology

Aug 21, 2024 · Operations

Designing a Disaster Recovery and Data Backup System for JD 秒送 LBS C‑End SOA Services

This article explores the design of a disaster‑recovery framework for JD’s秒送 LBS C‑end SOA services, detailing data‑backup strategies, cost‑reduction techniques, grid‑based caching using H3, diff validation, client‑side caching, and deployment modules to balance reliability, performance, and expense.

Backend ServicesCost OptimizationLBS

0 likes · 18 min read

Designing a Disaster Recovery and Data Backup System for JD 秒送 LBS C‑End SOA Services

JD Tech Talk

Aug 16, 2024 · Operations

Designing Cost‑Effective Disaster Recovery Data Backup for LBS‑Based SOA Services

This article details a comprehensive disaster‑recovery strategy for LBS‑driven SOA services, covering challenges of massive POI data backup, cost‑reduction via grid indexing (H3), selective caching, compression, diff validation, client‑side fallback, and deployment processes to achieve reliable, low‑cost data availability.

Cost OptimizationLBSdata backup

0 likes · 19 min read

Designing Cost‑Effective Disaster Recovery Data Backup for LBS‑Based SOA Services

Zhuanzhuan Tech

Mar 2, 2023 · Backend Development

Location-Based Services (LBS) in On‑Site Fulfillment: Geofencing, Order Allocation, Routing and Autonomous Scheduling

This article explains how a location‑based service platform uses geofencing, R‑tree indexing, ray‑casting algorithms, and Elasticsearch GEO queries to determine service eligibility, allocate on‑site engineers, plan routes, and enable autonomous order scheduling for a C2B recycling business.

ElasticsearchGeofencingLBS

0 likes · 12 min read

Location-Based Services (LBS) in On‑Site Fulfillment: Geofencing, Order Allocation, Routing and Autonomous Scheduling

IT Architects Alliance

Jan 6, 2022 · Fundamentals

Comprehensive Overview of Indoor Positioning Technologies and Their Challenges

This article provides a detailed overview of indoor positioning technologies—including GPS, cellular base‑station, Wi‑Fi, RFID, Bluetooth, UWB, infrared, visible light, geomagnetic, and visual methods—explaining their principles, advantages, limitations, and the specific challenges of achieving accurate, reliable, and cost‑effective indoor location services.

GPSIndoor PositioningLBS

0 likes · 25 min read

Comprehensive Overview of Indoor Positioning Technologies and Their Challenges

Sohu Tech Products

Jul 7, 2021 · Backend Development

Implementing Nearby‑People (LBS) with MySQL and Redis GEO

This article explains how to build a location‑based "nearby people" feature by storing coordinates in MySQL, filtering with rectangular bounds, calculating distances in Java, and then scaling the solution with Redis GEO using GeoHash and Sorted Sets for high‑performance proximity queries.

GeoHashLBSLocation Services

0 likes · 14 min read

Implementing Nearby‑People (LBS) with MySQL and Redis GEO

Didi Tech

Sep 17, 2020 · Artificial Intelligence

Machine Learning Practices in DiDi's Network Positioning: From Unsupervised Probabilistic Models to End‑to‑End CNN

DiDi’s network‑positioning system, which serves billions of daily location requests using Wi‑Fi and cellular signals, has evolved from an unsupervised probabilistic fingerprint matcher through a supervised GBDT‑DeepFM regression model to a fully end‑to‑end CNN that directly predicts coordinates, delivering markedly higher accuracy.

CNNDidiLBS

0 likes · 11 min read

Machine Learning Practices in DiDi's Network Positioning: From Unsupervised Probabilistic Models to End‑to‑End CNN

macrozheng

May 8, 2020 · Backend Development

Build an Efficient ‘Nearby Users’ Feature Using MySQL, Redis, MongoDB & GeoHash

This article explores multiple backend strategies for implementing a location‑based “nearby users” service, covering pure MySQL queries, MySQL combined with GeoHash, Redis Geo commands, and MongoDB geospatial indexes, explaining design principles, pros and cons, and providing complete code examples for each approach.

GeoHashLBSMongoDB

0 likes · 20 min read

Build an Efficient ‘Nearby Users’ Feature Using MySQL, Redis, MongoDB & GeoHash

JD Retail Technology

Dec 27, 2019 · Artificial Intelligence

JD Health 'Medicine Urgent Delivery' Project: LBS‑Based Store Recommendation and Full‑Channel Transaction Architecture

The JD Health Medicine Urgent Delivery service leverages LBS‑driven store recommendation, full‑channel transaction integration, and AI‑powered recommendation models to deliver medicines within half an hour across more than 100 cities, showcasing cross‑team technical collaboration and operational innovations.

AIFull-ChannelJD Health

0 likes · 9 min read

JD Health 'Medicine Urgent Delivery' Project: LBS‑Based Store Recommendation and Full‑Channel Transaction Architecture