Context-Aware Data Query Suggestion Service
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Solution Overview
Problem
Existing data solutions fail to efficiently and accurately provide relevant data access from massive datasets in business enterprises, as they lack effective methods to utilize user-context information for suggesting next steps during data exploration.
Innovation Solution
A computer-implemented method that generates data query suggestions by determining a query context from user input, characteristics, and viewed data, using usage metrics to compute ranked scores, and suggesting compatible data combinations, including dimensions and measures, to provide relevant data suggestions in the user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated data solutions are implemented to access data from massive datasets, then data access accuracy is improved, but system complexity increases
Solution Approach 1:
The system automatically analyzes user behavior patterns, query contexts, and usage metrics to generate query suggestions without requiring manual configuration or complex user setup. The suggest query service self-adapts to user needs by monitoring interactions and autonomously providing relevant data access recommendations
Solution Approach 2:
The system implements feedback loops by tracking usage metrics associated with query suggestions and user interactions. This feedback mechanism allows the system to learn from user behavior patterns and continuously improve the relevance and accuracy of suggested queries, resolving the contradiction between accuracy and complexity through adaptive learning
2Ease of operation
If user-context information is utilized to provide query suggestions, then ease of operation is improved, but information processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from user-context information, including current query state, user profile characteristics, and recently viewed data. By selectively extracting key contextual elements rather than processing all available user data, the system maintains ease of operation while managing information processing requirements efficiently
Solution Approach 2:
The suggest query service applies different processing intensities to different types of user-context information. High-value contextual data such as current query state and active user profile receive intensive processing, while less critical information receives minimal processing. This localized quality approach optimizes the balance between operational ease and processing volume
Data Source
AI summary
A computer-implemented method for generating a plurality of data query suggestions is described. The method includes receiving a textual input in a user interface of a software application implementing a plurality of business processes and determining a query context for the textual input. The method also includes computing a plurality of ranked numerical scores based on the query context in which the ranked numerical scores are computed using information obtained from a plurality of usage metrics associated with the query context and determining, using the plurality of ranked numerical scores, a plurality of candidate data combinations. The method also includes ordering the plurality of candidate data combinations according to the plurality of ranked numerical scores and generating at least one data suggestion using the ordered plurality of candidate data combinations and providing the at least one data suggestion in the user interface.