Qunar Technology Carnival: Interviews on Search Optimization, AIOps Fault Localization, and Revenue Management

The Qunar Technology Carnival, organized by Qunar Academy, showcases four technical tracks (QClient, QData, QTest, QInfrarch) and includes a series of interview articles with speakers to promote internal technical expertise and industry influence.

In the interview with Wang Mingyou, a senior engineer from the QData team, he explains recent search ranking enhancements such as query understanding using NER and entity linking, a unified recall pipeline, and a lightweight Lucene NRT‑based architecture that improved performance and reduced maintenance overhead.

He Yang, a data development engineer, discusses his AIOps project for automatic fault localization, covering monitoring data processing, trace analysis, and anomaly detection, as well as the difficulties encountered and the iterative solutions applied to improve fault‑root identification.

Jia Ziyan, a data scientist from the airline ticket big‑data team, presents the application of revenue management in ticket pricing, describing how machine‑learning models and big‑data pipelines enable dynamic pricing, the implementation results in charter seat allocation and intelligent marketing, and future plans for broader product integration.

The article concludes that the carnival provided valuable knowledge exchange, exposing participants to cutting‑edge AI and data‑driven techniques, and includes links to interview videos and highlights.