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This article explains how to handle massive Excel‑DB import/export tasks in Java by comparing POI workbook types, selecting the right implementation, and leveraging EasyExcel with batch queries, sheet splitting, and JDBC batch inserts to process over three million rows efficiently.
This tutorial explains step-by-step how to register a Baidu Maps developer account, set up a PHP script, create an HTML page with a map container, retrieve geographic coordinates via JavaScript, call the Baidu Maps API to fetch nearby points of interest, and render them on the page.
This tutorial explains step‑by‑step how to register a Baidu Maps developer account, create a PHP script, build a simple HTML page, retrieve geographic coordinates via Ajax, call the Baidu Maps API to fetch surrounding POIs, and render them on the map for a web project.
This article explains how to efficiently import and export massive Excel datasets in Java by comparing POI implementations, selecting the appropriate workbook type, and using EasyExcel with batch processing, pagination, and JDBC transactions to achieve high performance for hundreds of thousands to millions of rows.
This article explains how to efficiently import and export massive datasets (up to millions of rows) between Excel and databases using Apache POI, SXSSF, and Alibaba's EasyExcel, comparing workbook types, outlining performance considerations, and providing Java code examples for batch processing, paging, and transaction management.
This article explains how to implement fast nearby POI queries by calculating distances with the Haversine formula, designing MySQL tables and indexes, applying GeoHash for dimensionality reduction, and leveraging Redis Geo for high‑concurrency scenarios, while also providing Go code examples for each step.
This article explains how to handle large‑scale Excel import and export in Java by comparing traditional POI workbooks, selecting the appropriate implementation based on data volume and requirements, and presenting a high‑performance solution with EasyExcel, batch processing, and JDBC transactions for hundreds of millions of rows.
This article demonstrates how to use EasyPOI to export Excel files with dynamically generated column headers and data rows in Java, covering dependency configuration, header creation, data population, merging cells, utility methods, DTO definitions, and a complete test case.
To efficiently filter unchanged POI signboards, the authors propose a multimodal image‑retrieval system that combines enhanced global and local visual features with BERT‑encoded OCR text, using metric learning and alignment techniques to achieve over 95 % accuracy while handling occlusion, viewpoint variation, and subtle text changes.
The 9th CCF Big Data & Computing Intelligence Contest partners with Gaode Map to launch a POI Name Generation challenge, requiring participants to fuse image, signboard detection, and OCR data to automatically produce accurate, fluent place names, with a ¥50,000 prize pool, weekly vouchers, and recruitment opportunities for global teams.
This article explains how to create a reusable Java utility for Excel import and export using custom annotations, POI library, and helper methods to handle data mapping, dropdown validation, and HTTP response streaming, providing step‑by‑step code examples for both operations.
Amap’s visual‑technology pipeline automatically generates and updates POI names by crowdsourcing street‑level images, applying deep‑learning scene‑text recognition, dual‑branch classification of text attributes, and a BERT‑plus‑graph‑attention model that selects and orders recognized text, achieving about 95 % naming accuracy.
This article presents a practical solution for eliminating POI label overlap on maps by applying 2D collision‑detection techniques and the A‑Star path‑finding algorithm to intelligently position labels and draw connector lines, complete with Python and OpenCV code examples.
Amap transformed its fragmented POI deep‑information pipelines into a unified platform that automates data acquisition, parsing, dimension alignment, specification mapping, and lifecycle management across billions of records, enabling product managers to integrate, debug, and scale diverse content‑provider feeds with real‑time, end‑to‑end control.
Amap's POI naming automation uses a two-stage cascade model: Stage 1 extracts token and sentence features with POS tags and domain-adapted BERT‑POI; Stage 2 employs a Bi‑LSTM to model line relationships, achieving over 95% semantic accuracy and 3‑6% recall improvements, thereby enhancing automatic signboard‑based POI name generation.
This article explains how to design a reusable Java utility for exporting large volumes of data to Excel using Apache POI, covering header configuration, data transformation, cell formatting, multi‑threaded data retrieval, asynchronous processing, and provides sample code and usage instructions.
The paper presents two inexpensive techniques for extracting geographic aliases of points of interest—comparing high‑dimensional contextual vectors of nearby shipping addresses and analyzing co‑occurring words in identical addresses—to construct a knowledge base that links official names with their synonyms, improving location‑based service accuracy.