Graphical Query Builder for Database Schema Navigation
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Solution Overview
Problem
Users without prior knowledge of database schema and query languages face difficulties in constructing efficient database queries, requiring expertise in database design and querying languages like SQL.
Innovation Solution
A graphical query system that allows users to build queries visually by selecting dataset icons and operations, with a translation engine converting the graphical queries into structured query language (SQL) for execution, using metadata to suggest operations and provide natural language expressions for intuitive query construction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users construct queries using traditional SQL language, then query execution efficiency is maintained, but user expertise requirement increases
Solution Approach 1:
The patent introduces a graphical query builder as an intermediary layer between the user and the database. This visual interface allows users to construct queries through drag-and-drop operations and visual assembly of query components, eliminating the need for SQL syntax knowledge. The graphical builder translates user-friendly visual queries into executable SQL statements, resolving the contradiction by maintaining ease of operation while managing system complexity through automated translation.
Solution Approach 2:
The query construction process is segmented into discrete visual components that can be independently selected and assembled. Users build queries by selecting individual elements (tables, columns, conditions) and combining them through visual operations rather than writing monolithic SQL statements. This segmentation makes query construction more accessible while the system handles the complexity of assembling these segments into valid SQL.
2Ease of operation
If graphical query interface is provided, then user accessibility improves, but query translation overhead increases
Solution Approach 1:
The system performs preliminary actions by pre-compiling and caching translation rules for graphical query components to SQL statements. Common query patterns are pre-analyzed and stored as translation templates, allowing rapid conversion of graphical queries to SQL without performing full translation analysis each time. This reduces the time overhead of query translation while maintaining user-friendly graphical interface capabilities.
Data Source
AI summary
A schema parser may determine one or more datasets of a database schema, wherein the datasets include one or more fields from a data source and represents data corresponding to the one or more fields. An input handler may receive a selection of one or more of the datasets via an interface. A translation engine may provide operations for refining the data of the selected datasets into a result set via a query, wherein the operations may be determined based on metadata defining relationships amongst the data sources and fields. A query engine may provide a graphical query including operational flow indicators indicating a directional flow of the query from the selected datasets with the selected operation to the result set.