Asynchronous Database Correlation for Anonymous User Identification
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Solution Overview
Problem
It is challenging to match anonymous user data across multiple websites or databases, as existing technologies struggle to correlate anonymous user identification data with personal identifying information effectively.
Innovation Solution
A system and method that involve a first server retrieving anonymous user identification data from a browser session, storing it in a user profile, and querying multiple databases to identify matches, allowing for the retrieval and storage of additional user information based on these matches, even if the initial data is not found in the primary database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If anonymous user identification data is stored in a primary database, then data retrieval speed is improved, but data completeness deteriorates when matches are not found in the primary database
Solution Approach 1:
The system segments the database into a primary database (for fast retrieval) and secondary databases (for comprehensive matching). Anonymous user identification data is first queried against the primary database, and if no match is found, the system segments the search by querying multiple secondary databases separately, thereby maintaining both fast retrieval and data completeness.
Solution Approach 2:
The system introduces an intermediary matching mechanism that coordinates between the primary database and multiple secondary databases. When anonymous user identification data is not found in the primary database, the intermediary process automatically queries secondary databases and retrieves additional matching data, ensuring data completeness without compromising the speed of the primary retrieval path.
2Loss of information
If multiple databases are queried to ensure complete user data matching, then data completeness is improved, but system complexity increases
Solution Approach 1:
The system divides the database architecture into a primary database and multiple secondary databases, each serving specific matching purposes. This segmentation allows the system to manage complexity by organizing databases hierarchically, where the primary database handles common queries and secondary databases handle specialized matching scenarios.
Solution Approach 2:
The system implements an automated matching mechanism that self-manages the complexity of querying multiple databases. When anonymous user identification data is entered, the system automatically determines which databases to query, performs the matching operations, and retrieves results without requiring manual intervention, thereby managing system complexity through self-service automation.
3Measurement precision
If asynchronous correlation is implemented across spatially separated databases, then user identification accuracy is improved, but processing time increases
Solution Approach 1:
The system implements periodic action by first performing a quick synchronous query against the primary database, and only if needed, periodically initiating asynchronous queries against secondary databases. This periodic approach allows the system to maintain fast initial response times while periodically conducting more thorough accuracy checks when required.
Solution Approach 2:
The system performs preliminary action by first querying the primary database for immediate matches before initiating any asynchronous correlation processes. This preliminary step filters out cases that don't require complex matching, thereby reducing the overall processing time while maintaining high identification accuracy for cases that do require asynchronous correlation.
Data Source
AI summary
A computer-implemented method including forming a first user information database stored on a first server by retrieving, from a browser session, a first piece of user information including at least local user identification data and storing the first piece of user information in a user profile of the first user information database. The method further includes querying the first user information database for a second piece of user information. Responsive to not identifying the second piece of user information in the first user information database, the method further includes querying a second user information database stored on a second server for the second piece of user information associated with the first piece of user information. The method further includes retrieving the second piece of user information from the second database and saving the second piece of user information to the user profile of the first user information database.