Dynamic Data Source Routing for Travel Systems
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Solution Overview
Problem
Computerized travel systems face challenges in quickly reacting to rapid variations in computation inquiries or crises, such as polling outages, while maintaining data access efficiency and routing decisions between various data sources storing availability information, especially when using lower-reliability data sources.
Innovation Solution
A method for dynamically selecting a data source based on a statistical indicator, such as a polling error rate, to adjust the access attempt rate to the inventory source, switching between an inventory source and an availability cache source to maintain high availability and reduce failures, using an exponential response function to update the decision rule in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses an inventory source to retrieve availability information, then data accuracy is improved, but polling error rate increases and system reliability deteriorates
Solution Approach 1:
The patent introduces a mediator component that sits between the inventory source and the GDS system. This mediator monitors polling error rates and dynamically adjusts the routing of availability requests, switching between direct inventory source access and alternative data sources based on current system conditions, thereby resolving the contradiction between data accuracy and system reliability
Solution Approach 2:
The system dynamically changes the parameter of data source selection based on polling error rate thresholds. When the polling error rate exceeds a defined threshold, the system transitions from preferring the inventory source to using alternative data sources, thus adapting to changing conditions and maintaining reliability while preserving data accuracy when possible
2Loss of time
If the system increases access attempts to the inventory source, then data freshness is improved, but polling error rate increases and service quality deteriorates
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors polling error rates and uses this information to adjust the frequency and timing of inventory source access. The system reduces access attempts when error rates are high and increases them when error rates are low, creating a self-regulating system that balances data freshness with service quality
Solution Approach 2:
The system dynamically adjusts its behavior based on real-time conditions. The access pattern to the inventory source is not fixed but changes continuously according to monitored polling error rates, allowing the system to optimize between data freshness and service quality under varying operational conditions
3Speed
If the system uses multiple data sources in parallel, then response time is reduced, but system complexity increases
Solution Approach 1:
The patent segments the data source selection process into distinct, manageable components. The system divides availability requests into different routing paths based on simple criteria (polling error rate thresholds), making the complexity manageable through structured segmentation of the decision-making process
Solution Approach 2:
The system applies different quality levels to different data sources based on current conditions. Rather than treating all data sources uniformly, the system selectively uses higher-quality sources (inventory source) when conditions permit and lower-quality sources (alternative data sources) when necessary, optimizing response time while managing complexity through localized quality adjustments
Data Source
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AI summary
A computer-implemented reservation method and a corresponding system are utilized for controlling execution of a decision process to maintain data access efficiency upon receipt of a computation inquiry. The method comprises associating to a computer backend machine a configuration file containing at least a decision rule that drives the decision process and that is computed at least from a current value of a statistical indicator and a target value of the statistical indicator; periodically obtaining an updated value of the statistical indicator; upon detection that the updated value is differing from the target value, dynamically updating the configuration file and storing in real-time a recomputed decision rule in the configuration file.