Highlight Query Generation for Graph Data Analysis
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Solution Overview
Problem
Analysts face challenges in efficiently exploring and identifying significant patterns within graph data structures, as existing tools lack the ability to automatically generate and execute highlight queries that are tailored to individual user behaviors and patterns.
Innovation Solution
A computer-implemented method that analyzes data using schemas to identify patterns and frequencies, storing them in a global patterns structure, and then scores and selects patterns for each user based on their behavior, executing these as highlight queries to generate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual query construction is used for graph exploration, then analysts can customize queries for specific needs, but the process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary analysis of graph data to pre-identify significant patterns and structures before the analyst needs to query. By analyzing the graph data in advance and storing pattern information, the system prepares query templates that can be quickly deployed when needed, eliminating the time-consuming manual query construction process while maintaining customization capability
Solution Approach 2:
The system automatically generates highlight queries by analyzing user behavior patterns and graph data structures without requiring manual intervention. The query generation process serves itself by using accumulated pattern knowledge to create relevant queries automatically, significantly improving productivity while reducing the time analysts spend on query construction
2Adaptability or versatility
If generic query tools are used, then implementation is simple, but they cannot provide personalized insights for individual users
Solution Approach 1:
The system segments the query generation process into distinct functional modules: pattern identification, frequency analysis, significance scoring, and query construction. Each module handles a specific aspect of the analysis, allowing the complex adaptive functionality to be built from manageable components. This modular structure enables personalized insights while keeping the overall system architecture organized and maintainable
Solution Approach 2:
The system applies different analysis methods and criteria to different aspects of the graph data based on local characteristics. By analyzing patterns, frequencies, and significances specific to each user's data and behavior patterns, the system provides customized insights tailored to individual needs rather than applying a one-size-fits-all approach, achieving high adaptability through localized analysis quality
3Measurement precision
If comprehensive pattern analysis is performed on all data, then all significant patterns are identified, but the computational resources and time required increase significantly
Solution Approach 1:
The system performs partial analysis by focusing computational resources on identifying the most significant patterns rather than analyzing every possible pattern in the graph data. By using significance scoring to prioritize patterns based on their relevance and frequency, the system achieves high measurement precision for the most important patterns while consuming fewer computational resources, avoiding the need to exhaustively analyze all potential patterns
Data Source
AI summary
Provided are techniques for generating highlight queries. Data is analyzed using one or more schemas to identify patterns and frequencies of the patterns in the data. The patterns and the frequencies of the patterns are stored in a global patterns structure. A graph is analyzed to identify one or more of the patterns in the global patterns structure that are present in the graph and in which frequencies. The identified one or more of the patterns in the global patterns structure that are present in the graph are added to a per-user patterns structure with the frequencies of those patterns. The patterns in the per-user patterns structure are scored. A subset of the patterns in the per-user patterns structure that have scores above a threshold are selected as highlight queries. The selected subset of the patterns are executed as highlight queries to generate results, and the results are returned.