Query Generator With Replaceable Tokens For Database Reporting
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Solution Overview
Problem
Existing query generation methods for relational databases are inflexible, requiring separate queries for different filtering and calculation operations, leading to increased effort and storage needs, especially in large data warehousing applications where managing extensive data results is challenging.
Innovation Solution
A query generator that analyzes input queries for replaceable tokens, allowing users to input parameters for conditional expressions and calculations, generating diverse reports from a single query, and utilizing persistent parameter definitions to validate and manage user input, reducing data inconsistency and administrative burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate queries are created for different filtering and calculation operations, then the query can operate in exactly the same way each time it is executed, but the effort required to build and maintain the queries increases significantly and storage capacity requirements increase
Solution Approach 1:
The patent implements a single query template that can perform multiple filtering and calculation operations by accepting different parameter inputs. Instead of creating separate queries for each operation, the system uses a universal query structure with parameter placeholders that can be dynamically instantiated with different values, achieving multi-functionality while reducing complexity
Solution Approach 2:
The patent uses parameter substitution to transform a single query template into multiple specific queries. By defining parameters with default values and allowing dynamic parameter assignment, the system can adapt the same query structure to different filtering and calculation needs without modifying the core query logic, thus maintaining consistency while reducing management effort
2Productivity
If a single query template with parameters is used, then the effort to build and maintain queries is reduced, but the query becomes more complex in terms of parameter management and validation
Solution Approach 1:
The patent performs preliminary actions by pre-defining parameter specifications, default values, and validation rules before the query is executed. The system prepares parameter templates with metadata that describes expected data types, constraints, and default values, so that when the query runs, parameter validation and substitution can proceed automatically without manual intervention, thus reducing complexity despite the single-template approach
Solution Approach 2:
The patent implements feedback mechanisms through parameter validation that check user inputs against predefined constraints and data types. The system provides feedback to users about parameter validity, required formats, and acceptable ranges, ensuring that parameter substitutions maintain query correctness. This automated validation reduces the burden of parameter management by preventing errors before they affect query execution
3Quantity of substance
If extensive data results are returned from large database tables, then all available data is retrieved, but the results become difficult to manage and system resources are severely occupied
Solution Approach 1:
The patent applies local quality by allowing different parameters to control different aspects of data retrieval and filtering. Instead of retrieving all data uniformly, the system uses localized parameter substitutions to apply specific filtering criteria to different parts of the query (e.g., date ranges, category filters, numerical thresholds), enabling selective data retrieval that maintains completeness for needed data while reducing overall volume by filtering out unnecessary records
4Loss of information
If hard copy reports are generated at regular time intervals, then continued flow of information is provided, but the creation process is time consuming and requires considerable expense
Solution Approach 1:
The patent creates a universal query template that can serve multiple reporting needs through parameter substitution. The same base query structure can generate different reports by changing parameter values (e.g., different time periods, different data filters, different calculation parameters), eliminating the need to create separate queries for each report. This multi-functionality maintains information continuity across regular reports while dramatically reducing creation time and expense
Data Source
AI summary
A query generator is operable to generate a query for retrieving data that satisfy a conditional expression from a database, and/or perform an arbitrary calculation on at least part of the retrieved data. The query generator can receive an input query and analyze the query to ascertain whether it contains a replaceable token. If the query contains a replaceable token, the query generator can replace that token with user input specifying a parameter of the conditional expression and/or the arbitrary calculation, thereby generating an output query for retrieving the data that satisfy the conditional expression and/or performing the arbitrary calculation on the retrieved data.