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data collection

110 articles · Page 2 of 2
Meitu Technology
Meitu Technology
Dec 19, 2017 · Big Data

Meitu Internet Technology Salon Session 7: Practices in Recommendation Algorithms, Big Data, and Personalized Recommendation

At Meitu’s seventh Internet Technology Salon in Xiamen, over a hundred experts discussed recommendation algorithms and big‑data solutions, with talks on the Arachnia log‑collection system, the Naix distributed bitmap service, Meitu’s personalized recommendation pipeline challenges, and novel data‑missing‑theory models for improved performance.

big datadata collectiondistributed bitmap
0 likes · 8 min read
Meitu Internet Technology Salon Session 7: Practices in Recommendation Algorithms, Big Data, and Personalized Recommendation
58 Tech
58 Tech
Dec 15, 2017 · Big Data

Design and Architecture of WMDA: A Comprehensive User Behavior Analysis Platform

The article details WMDA, a no‑code and manual‑code data collection platform for PC, mobile and app that supports real‑time and offline user behavior analysis, describing its functional model, behavior taxonomy, five‑layer architecture, tracking techniques, circle‑selection, data services, streaming and batch processing pipelines, and related technologies such as Storm, Spark, Druid and Roaring Bitmap.

DruidRoaring Bitmapbig data
0 likes · 18 min read
Design and Architecture of WMDA: A Comprehensive User Behavior Analysis Platform
Baidu Intelligent Testing
Baidu Intelligent Testing
Oct 9, 2017 · Big Data

User Behavior Analysis: From Data Acquisition to Funnel Insights

The article explains how to move beyond macro app metrics by collecting offline and real‑time user data, storing it in HDFS, processing it with Spark, visualizing behavior paths as state‑machine trees, and performing branch‑funnel analysis to uncover conversion bottlenecks and improve product quality.

AnalyticsFunnel Analysisbig data
0 likes · 5 min read
User Behavior Analysis: From Data Acquisition to Funnel Insights
Qunar Tech Salon
Qunar Tech Salon
Aug 18, 2017 · Operations

Hardware Automation Operations System at Qunar: Design, Implementation, and Lessons Learned

This article details Qunar's hardware automation operations platform, covering the hardware scope, pain points of manual processes, a five‑stage lifecycle, automated testing, data collection, fault handling, and the underlying Mesos‑Marathon‑Docker infrastructure that together improve efficiency, reliability, and cost control.

data collectionfault handlinghardware automation
0 likes · 21 min read
Hardware Automation Operations System at Qunar: Design, Implementation, and Lessons Learned
Ctrip Technology
Ctrip Technology
May 18, 2017 · Backend Development

Design and Implementation of Ctrip's Real-Time User Data Collection System

This article details the design, technology selection, architecture, encryption, compression, and performance evaluation of Ctrip's real-time user data collection system, which leverages Java, Netty, Kafka, and Avro to achieve high throughput, low latency, and robust fault tolerance for mobile and web applications.

Nettybackend developmentdata collection
0 likes · 17 min read
Design and Implementation of Ctrip's Real-Time User Data Collection System
Liulishuo Tech Team
Liulishuo Tech Team
Aug 6, 2016 · Product Management

Structuring and Managing Data Collection Requirements with JSON and Git

By defining data collection (event tracking) requirements in a structured JSON format and storing them in Git with a web interface that abstracts version control, teams can standardize identifiers, validate data formats automatically, track changes via commit logs, and streamline collaboration between product managers, developers, and testers.

Data ValidationGitJSON
0 likes · 7 min read
Structuring and Managing Data Collection Requirements with JSON and Git
Baidu Intelligent Testing
Baidu Intelligent Testing
Apr 12, 2016 · Product Management

User Feedback Analysis: Methods, Process, and Core Metrics

This article explains what user feedback is, why it should be analyzed, and provides a step‑by‑step methodology—including channel setup, data collection, coding, categorization, and statistical analysis—along with key performance indicators for monitoring feedback handling in product management.

categorizationdata collectionfeedback analysis
0 likes · 8 min read
User Feedback Analysis: Methods, Process, and Core Metrics