Flexible SQL Column Selection via Expansion Clause
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Solution Overview
Problem
Traditional SQL SELECT statements are limited in selecting specific columns, often requiring complex and ungainly statements when using SELECT *, and specifying each column and transformation function, which is time-consuming and difficult to construct.
Innovation Solution
The introduction of an EXPAND clause in SQL that allows for flexible column selection, transformation, and naming, enabling the selection of multiple columns with regular expressions, transformations, and renaming, simplifying the construction of SQL statements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If SELECT * is used to retrieve all columns, then the user does not need to specify each column individually, but unnecessary data is retrieved and transformation functions cannot be applied
Solution Approach 1:
The patent segments the column selection process by introducing an expansion clause that divides columns into expandable groups. Instead of selecting all columns with SELECT *, users can specify expansion clauses that automatically expand to include only necessary columns based on defined criteria, thereby avoiding retrieval of unnecessary data while maintaining ease of operation.
Solution Approach 2:
The patent applies preliminary action by pre-defining expansion clauses with transformation functions that will be automatically applied to selected columns. This allows the system to pre-process and transform data according to predefined rules before retrieval, enabling transformation functions to be applied without requiring explicit specification of each column while still avoiding unnecessary data retrieval.
2Adaptability or versatility
If specific columns are listed in SELECT statement, then transformation functions can be applied to individual columns, but the statement becomes complex and time-consuming to construct
Solution Approach 1:
The patent merges column selection with transformation function application by combining them into a single expansion clause syntax. Instead of listing each column individually with its transformation function, users can define an expansion clause that automatically applies transformations to all columns within the expanded group, thereby reducing SQL statement complexity while maintaining full transformation capability.
Solution Approach 2:
The expansion clause serves multiple functions simultaneously: it selects columns, applies transformation functions, and handles column naming conventions. This multi-functional approach eliminates the need for complex, lengthy SELECT statements while preserving the ability to apply transformations to multiple columns through a single unified syntax.
3Manufacturing precision
If every column and transformation is explicitly specified, then precise control over selected data is achieved, but the SQL statement becomes ungainly and difficult to maintain
Solution Approach 1:
The patent applies preliminary action by pre-defining expansion clauses that encapsulate column selection criteria and transformation rules. These pre-defined clauses maintain precise control over data selection by specifying exact inclusion criteria, while simultaneously improving statement construction ease by replacing lengthy, complex column lists with concise expansion clause references.
Solution Approach 2:
The expansion clauses provide dynamic column selection based on predefined criteria rather than static column lists. This allows the system to automatically adapt which columns are selected and transformed based on the expansion clause definitions, maintaining precision while reducing the manual effort required to construct and maintain SQL statements.
Data Source
AI summary
An approach is provided to expand attributes included in an SQL expansion clause into a number of selection statements. In the approach, an expansion clause is identified in within a Structured Query Language (SQL) statement with the SQL statement identifying a relational database table. Column attributes associated with the identified relational database table are compared to attributes included in the identified expansion clause. Columns included in the relational database table are then selected based on the comparison. SQL column selection statements are then generated with each of the generated SQL column selection statements corresponding to one of the selected columns. The generated SQL column selection statements are then included in the SQL statement.