Task-Specific Graph Analysis Using Simplified Data Representations
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Solution Overview
Problem
Existing graph analysis and visualization techniques are inadequate for complex data instances, such as textual and protein data, as they rely on generic and inefficient graph property computation, making it difficult to visualize and analyze these datasets effectively.
Innovation Solution
A method involving simplification and transformation rules to convert complex data into simplified and graph representations, followed by the execution of customized lambda functions to determine instance properties, enabling task-specific analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic graph property computation is used for complex data instances, then the analysis can be performed, but the efficiency and accuracy are insufficient
Solution Approach 1:
The patent segments the analysis process into distinct phases: simplification phase (converting complex data to simplified representations) and transformation phase (converting to graph representations). This segmentation allows each phase to be optimized independently, improving overall efficiency while maintaining accuracy through specialized processing for each data type.
Solution Approach 2:
The patent changes the parameters of data representation by applying domain-specific simplification rules that transform complex data instances into simplified representations with optimized properties. This parameter change enables more efficient computation while preserving the essential characteristics needed for accurate analysis.
2Ease of operation
If complex data instances are visualized individually or collectively using generic methods, then visualization is possible, but the complexity increases and effectiveness decreases
Solution Approach 1:
The patent introduces simplified representations as an intermediary form between complex data instances and graph representations. This intermediary simplifies the data structure, making it easier to handle and visualize while maintaining the essential information needed for accurate analysis and comparison.
Solution Approach 2:
The visualization process is segmented into separate stages: first converting complex data to simplified representations, then to graph representations. This segmentation reduces the complexity at each stage, making the overall process more manageable and effective.
3Measurement precision
If domain-specific analysis is performed on complex data, then the accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies domain-specific simplification rules that change the parameters of complex data instances into simplified representations optimized for their specific domains. This parameter transformation maintains accuracy for domain-specific analysis while reducing the computational complexity of processing.
Solution Approach 2:
The patent applies different simplification and transformation rules tailored to specific domains (e.g., molecular biology, chemistry). This local quality approach ensures that each domain receives specialized processing optimized for its specific requirements, improving accuracy without uniformly increasing complexity across all data types.
Data Source
AI summary
In an embodiment, a set of data instances associated with a domain is received. A set of simplification rules is applied on the received set of data instances. A set of simplified representations associated with the received set of data instances is determined. A set of transformation rules on the is applied received set of data instances. A set of graph representations associated with the received set of data instances is determined. A set of first lambda functions is executed on a set of representations. The set of representations includes at least one of the determined set of simplified representations or the determined set of graph representations. A set of instance properties associated with the set of representations is determined. The set of representations is analyzed. First information associated with the set of representations is rendered based on the analysis of the set of representations.