Travel Database Maintenance with Extractors and Data Keepers
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Solution Overview
Problem
Travel booking systems face challenges in maintaining up-to-date and accurate databases due to rapidly changing information from diverse sources, requiring efficient data management and search capabilities to provide reliable booking options.
Innovation Solution
Implementing a system with multiple canonical databases managed by CDMs, using extractors, data keepers, and replicators to autonomously update and curate data, ensuring each database updates independently and reducing bandwidth by processing data at a low level, with modular architecture for easy addition or removal of data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a single centralized database is used to store all travel information from diverse sources, then data completeness is improved, but system complexity and difficulty in maintaining data accuracy increase
Solution Approach 1:
The patent divides the centralized database into multiple independent canonical databases, each managed by a dedicated CDM for a specific travel category (flights, hotels, car rentals, etc.). This segmentation allows each database to be maintained independently with specialized data processing logic, reducing overall system complexity while maintaining data completeness across all categories.
Solution Approach 2:
The patent introduces data keepers as intermediary components that sit between the extractors and canonical databases. These data keepers pre-process and validate data before it enters the canonical databases, reducing the maintenance burden on the databases themselves and simplifying the overall system architecture.
2Loss of time
If data from multiple diverse sources is continuously integrated into a single database, then information currency is improved, but bandwidth consumption and processing overhead increase
Solution Approach 1:
By segmenting data processing into category-specific canonical databases, the system can process and update only the relevant data subsets that have changed, rather than continuously processing all travel information. This reduces bandwidth consumption while maintaining information currency for each travel category.
Solution Approach 2:
Data keepers perform preliminary data validation, deduplication, and formatting before data enters the canonical databases. This pre-processing reduces the volume of data that needs to be stored and processed subsequently, lowering bandwidth consumption and processing overhead while ensuring data quality.
3Loss of information
If a monolithic database structure is used, then data integration is improved, but flexibility to add or remove data sources deteriorates
Solution Approach 1:
The patent creates independent canonical databases for different travel categories, each with its own CDM and data processing pipeline. This modular architecture allows new data sources to be added by creating new extractors and data keepers for specific categories without affecting other parts of the system, greatly improving flexibility while maintaining comprehensive data integration.
Solution Approach 2:
The patent implements a universal framework with standardized components (extractors, data keepers, canonical databases) that can handle multiple travel categories. This universal architecture maintains data integration across diverse sources while providing the flexibility to easily adapt to new data sources and categories through configuration rather than structural changes.
4Loss of information
If all travel information is processed and stored in a single database, then search comprehensiveness is improved, but search performance and query response time worsen
Solution Approach 1:
By dividing the database into category-specific canonical databases (flights, hotels, car rentals, etc.), search queries can be routed to only the relevant database segments rather than searching the entire database. This segmentation maintains comprehensive search coverage across all travel categories while significantly improving query response time by reducing the search scope.
Data Source
AI summary
This disclosure describes systems, software, and computer implemented methods for maintaining travel databases and providing improved search results from them. Implementations include querying a plurality of data sources using a plurality of extractors. The plurality of extractors can receive travel information from the plurality of data sources and populate a software object with the travel information to generate structured travel information which can be submitted to an extraction queue. A data keeper can extract structured travel information of the particular category from the extraction queue and submit the structured travel information to a database queue. A canonical database manager (CDM) can extract the structured travel information of the particular category from the database queue.