User-Defined Query Logic Storage for Database Filtering
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Solution Overview
Problem
Existing systems face challenges in efficiently filtering entity data in databases using user-defined query logic, particularly in evaluating multiple segments simultaneously while avoiding SQL injection attacks.
Innovation Solution
The system creates data tables to store user-defined segment definitions and criteria, transforms these definitions into sets of filter definition tuples, and composes a user-defined query that interprets these tuples as native database operations, allowing for simultaneous evaluation of multiple segments in a single SQL query.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user-defined query logic is stored in a database for filtering entity data, then filtering efficiency and simultaneous segment evaluation are improved, but system complexity increases
Solution Approach 1:
The system segments query logic into distinct components: segment definitions, criteria, and filter definition tuples are stored as separate data tables. This allows the complex query composition process to be broken down into manageable steps where each table serves a specific function in the overall filtering operation.
Solution Approach 2:
The patent introduces an intermediary transformation layer that converts user-defined segment definitions into filter definition tuples, which are then interpreted as native database operations. This intermediary format acts as a bridge between user-friendly segment definitions and efficient database queries, resolving the complexity by providing a standardized intermediate representation.
2Loss of time
If multiple segments are evaluated simultaneously in a single SQL query, then processing time is reduced, but vulnerability to SQL injection attacks increases
Solution Approach 1:
The system extracts and stores query logic parameters (filter definition tuples) in the database separately from the query execution mechanism. By taking out the query construction step and storing it as pre-defined logic, the system eliminates the need to assemble SQL queries from user-provided strings, thereby removing the SQL injection vulnerability while maintaining simultaneous evaluation capability.
Solution Approach 2:
The patent converts the potential harm of SQL injection by using a safe alternative: instead of constructing queries from user strings, the system uses a query language that is safe to execute even when parameters come from user input. The filter definition tuples are interpreted as native operations, transforming a vulnerable approach into a secure one while preserving performance benefits.
3Ease of operation
If user-defined segment definitions are transformed into sets of filter definition tuples, then query composition becomes more manageable, but transformation complexity increases
Solution Approach 1:
The system changes the parameter representation from natural language segment definitions to a standardized set of filter definition tuples with specific syntax and structure. This parameter transformation makes query composition more manageable by providing a consistent format that can be systematically processed, while the transformation itself is encapsulated in a dedicated component that handles the complexity.
Data Source
AI summary
Apparatuses, methods, and systems for filtering entities within a database based on rules of a user-defined segment definition. One method includes creating data tables for storing user-defined segment definitions in the database, the data tables including the user-defined segment definitions and criteria, receiving new user-defined segment definitions, transforming the new user-defined segment definitions, storing the transformed new user-defined segment definitions in the database, composing a user-defined query based on the transformed new user-defined segment definitions as stored in the database, wherein the user-defined query contains logic that interprets the filter definition tuples as native operations provided by the database, and running the composed user-defined query against the data tables to determine which of the entities qualify for membership in new segments based on each of the new user-defined segment definitions as determined by the logic contained in the composed user-defined query.