Pre-computed Travel Cache with Adaptive Refresh for Accuracy
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Solution Overview
Problem
Current reservation systems face inefficiencies in providing accurate and up-to-date air travel recommendations due to high operational costs and volatility in air travel prices, leading to low cache accuracy and limited user flexibility in search parameters.
Innovation Solution
A distributed reservation system that maintains a cache of pre-computed travel recommendations with assigned refresh frequencies, utilizing multiple data analysis engines to optimize data pre-computations and merge identical travel recommendations, ensuring high cache accuracy at reduced costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If pre-computed travel recommendations are maintained in cache to enable fast response times, then response speed is improved, but cache accuracy deteriorates due to price volatility
Solution Approach 1:
The system implements periodic refreshes of cached travel recommendations at scheduled intervals. The refresh frequency is determined by a score assigned to each recommendation based on factors like price volatility and demand patterns. This periodic action ensures cache data remains relatively current while avoiding continuous recomputation, thus maintaining both fast response times and acceptable accuracy levels.
Solution Approach 2:
The system pre-computes travel recommendations and stores them in cache before users actually search for them. This preliminary action allows the system to have ready-made recommendations available instantly when users query, providing fast response times. The pre-computed data includes predicted prices and availability based on historical patterns and current market conditions.
2Measurement precision
If cache refresh frequency is increased to improve accuracy, then cache accuracy is improved, but hardware resource consumption increases
Solution Approach 1:
The system applies different refresh frequencies to different travel recommendations based on their individual characteristics. Recommendations with high price volatility or high demand receive more frequent refreshes (higher local quality of accuracy), while stable, low-demand routes receive less frequent refreshes. This localized differentiation optimizes overall cache accuracy while minimizing unnecessary hardware resource consumption on stable routes.
Solution Approach 2:
Instead of refreshing all cached recommendations at maximum frequency, the system performs partial refreshes only on the most critical or volatile recommendations. The refresh score determines which recommendations receive full attention versus those that can wait. This partial action approach achieves acceptable overall accuracy without the excessive hardware resource usage that would result from uniformly high refresh frequencies across all data.
3Adaptability or versatility
If comprehensive travel data is pre-computed and stored to provide wide search flexibility, then user freedom in search parameters is improved, but system complexity and operational costs increase
Solution Approach 1:
The system segments the vast travel database into manageable portions organized by origin city, destination, date ranges, and other key parameters. This segmentation allows the system to store and retrieve specific subsets of data efficiently without needing to process the entire database. Each segment can be independently maintained and refreshed, reducing overall system complexity while preserving comprehensive search capabilities.
Solution Approach 2:
The system organizes travel recommendations in a multi-dimensional structure that allows efficient querying across multiple parameters simultaneously. By indexing data across multiple dimensions (origin, destination, date, price range, etc.), the system enables flexible searches without requiring complex ad-hoc processing. This dimensional organization reduces operational complexity while maintaining comprehensive search flexibility.
Data Source
AI summary
The method according to a preferred embodiment of the present invention provides a pre-shopping reservation tool which allows storing entire catalogs of air travels from many providers, while ensuring a high cache accuracy at limited operational costs. The system makes use of the fares knowledge to merge identical travel recommendations coming from different travel providers (airline, travel agencies) when possible, i.e. when fares are public and not negotiated for a specific travel provider. This prevents redundant prices to be stored in the system and improves its cost effectiveness. The system according to a preferred embodiment of the present invention relies on several dedicated data analysis engines to optimize the cost of data pre-computations while at the same time maintaining good data accuracy.