Query Suggestions via Contextual Data Analysis
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Solution Overview
Problem
Users face difficulties in constructing effective queries when connecting to external data sources, requiring understanding of data organization, field names, and table relationships, which can lead to frustration and abandonment of query-building efforts.
Innovation Solution
Providing query suggestions based on non-query data, such as identity, profile, and contextual information, to assist users in creating complete and appropriate database queries, with additional suggestions generated from query data and prior usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users build queries manually by understanding data organization and field names, then query accuracy can be improved, but the complexity and time required for query creation increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating query suggestions based on user profile data, contextual information, and historical query patterns before the user needs to construct the full query. This preliminary generation of query structures and suggestions reduces the complexity of manual query building while maintaining accuracy through AI-driven recommendations
Solution Approach 2:
An intermediary AI system acts as a mediator between the user's information needs and the data source. The system translates user context and requirements into structured query suggestions, serving as an intermediate layer that handles the complexity of query construction while presenting simplified options to the user
2Loss of information
If users manually construct queries with detailed knowledge of data sources, then complete information retrieval can be achieved, but the time required for query creation increases
Solution Approach 1:
The system performs preliminary analysis of user context, profile data, and historical patterns to generate ready-to-use query suggestions before the user needs to create the query. This preliminary work captures the essential information retrieval requirements automatically, reducing both the time needed and potential information loss
Solution Approach 2:
The system uses feedback from historical query data, user profiles, and contextual information to continuously improve query suggestions. By analyzing past successful queries and user behavior patterns, the system learns to provide more accurate and complete query recommendations over time, reducing information retrieval gaps
3Loss of information
If users explore large data sources to locate fields of interest, then comprehensive data understanding can be achieved, but the effort and complexity required increases
Solution Approach 1:
The system performs preliminary exploration and analysis of the data source structure, field relationships, and available data before the user needs to search for fields. By pre-processing and understanding the data organization through AI analysis, the system presents relevant field suggestions without requiring users to manually explore large data sources
Solution Approach 2:
An intermediary AI system mediates between the user's information needs and the complex data source structure. The system translates user context into relevant field and table suggestions, handling the complexity of data exploration internally while presenting simplified, context-relevant options to the user
Data Source
AI summary
Technologies are described herein for providing query suggestions. In order to provide a query suggestion, access may be detected at a data source. An initial query suggestion for a query of the data source may then be identified based, at least in part, on non-query data. The initial query suggestion may then be returned for display in a user-interface (UI) element. Additional query suggestions might also be provided based upon non-query data in response to a modification of the query.