Incomplete Query Processing via Parameter Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users unfamiliar with database query languages face inefficiencies when accessing database information due to complex interfaces, requiring expert assistance and causing bottlenecks in workflows, as they need to provide all query parameters, which can be missing or unknown.
Innovation Solution
A simplified web-based interface that allows users to submit query parameters, where a query processor identifies missing parameters by analyzing provided information and external data, such as user profiles or query history, and selects likely values based on predefined rules, heuristics, or statistical models to execute the query and present results effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users are provided with a simplified web-based interface for database queries, then ease of operation is improved, but the interface still requires information from users that they do not know, forcing them to go back to experts
Solution Approach 1:
The system performs preliminary action by proactively inferring and filling in missing query parameters before the query is executed. The query processor analyzes the provided parameters, identifies missing ones, and automatically determines appropriate values based on data types, default values, or contextual information, so that the complete query is ready for execution without requiring user intervention.
Solution Approach 2:
The query processor performs self-service by autonomously detecting missing parameters and generating appropriate values without user assistance. The system examines the query structure, data schemas, and contextual information to independently determine what parameters are missing and how to fill them, eliminating the need for users to consult experts for incomplete queries.
2Reliability
If users are required to provide all query parameters, then query completeness is improved, but device complexity increases and users unfamiliar with database query languages find the interface complex and intimidating
Solution Approach 1:
The query processor acts as an intermediary between the user and the database. It receives the user's simplified query input, automatically infers missing parameters, validates the complete query structure, and then executes the query against the database. This intermediary layer handles the complexity of parameter completion and query validation, shielding users from the underlying database complexity.
Solution Approach 2:
The system changes the state of query parameters from incomplete user input to complete validated parameters. By automatically inferring missing parameters and transforming the query structure, the system maintains query completeness while presenting a simplified interface to users, effectively changing the parameter state without user awareness.
3Measurement precision
If experts manually assist users with database queries, then query accuracy is improved, but expert time is consumed and workflow efficiency decreases
Solution Approach 1:
The query processor performs self-service by automatically detecting missing parameters, determining appropriate values, and completing queries without requiring expert intervention. This autonomous capability maintains query accuracy while eliminating the time experts would otherwise spend assisting users with incomplete queries, thereby improving workflow efficiency.
Solution Approach 2:
The system uses feedback from analyzing provided query parameters, data schemas, and contextual information to automatically determine missing parameter values. This feedback loop enables the system to learn from the query context and generate appropriate completions, maintaining accuracy while operating autonomously without expert involvement.
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable storage media for executing a database query based on an incomplete query. An example system configured to practice the method receives a web request, wherein a uniform resource locator (URL) of the web request provides an argument, and extracts the argument from the URL as a parameter for a database query. The system identifies an unspecified parameter in the database query, and selects a likely value for the unspecified parameter. Then the system executes the database query using the parameter and the likely value, and incorporates at least part of the query result in a web page served in response to the web request. The system can cache the web request and the likely value for the unspecified parameter for later use.