Data Perspective Generation for Flexible Analysis
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Solution Overview
Problem
Current data processing methods for exploring and analyzing data sets are complex, resource-intensive, and time-consuming, especially when dealing with large data sets or varying levels of granularity, requiring significant expertise and resources for model design, implementation, and changes, which limits user flexibility and efficiency.
Innovation Solution
An apparatus and method for data perspective generation and visualization that includes a processor and memory with computer-coded instructions to identify a processable data set, generate perspective data objects, and create a hierarchical structure using anomaly detection models, allowing for flexible exploration and visualization without requiring extensive model redesign.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing methods are used to explore and analyze data sets, then analysis depth and model accuracy can be achieved, but the process becomes complex, resource-intensive, and time-consuming
Solution Approach 1:
The patent segments the data set into multiple perspectives, where each perspective represents a specific view or aspect of the data. This segmentation allows complex data analysis to be broken down into manageable, independent perspective analyses, reducing overall process complexity while maintaining comprehensive analysis depth
Solution Approach 2:
The patent introduces perspective data objects as intermediary elements between the raw data set and the analysis models. These perspective data objects serve as mediators that pre-process and organize data from different viewpoints, simplifying the subsequent analysis process and reducing the complexity of model design and implementation
2Quantity of substance
If traditional data processing methods are used with large data sets, then comprehensive analysis can be performed, but significant resources and time are required
Solution Approach 1:
The patent divides large data sets into multiple perspective data objects, each representing a specific viewpoint or aspect. This segmentation enables parallel processing of different perspectives, significantly improving processing efficiency while maintaining the ability to analyze comprehensive data sets
Solution Approach 2:
The patent allows users to select and analyze only specific perspectives from the generated perspective data objects, rather than processing the entire data set uniformly. This partial action approach enables efficient analysis by focusing computational resources on relevant perspectives while maintaining the option to expand to comprehensive analysis when needed
3Adaptability or versatility
If data analysis models are redesigned to accommodate changing user needs, then analysis flexibility is improved, but the process requires extensive re-design and re-training
Solution Approach 1:
The patent creates a universal framework where perspective data objects serve as multi-functional intermediaries that can adapt to different user needs and analysis requirements. The system generates a comprehensive set of perspectives that can be selectively applied to various analysis scenarios, providing flexibility without requiring model re-design for each new requirement
Solution Approach 2:
The patent performs preliminary action by pre-generating a comprehensive set of perspective data objects from the input data set before actual analysis begins. This pre-computation creates a flexible foundation that can be quickly adapted to different user needs by simply selecting different perspectives, eliminating the need for time-consuming re-design and re-training when requirements change
Data Source
AI summary
Embodiments of the present disclosure provide apparatuses, methods, computer program products, and systems for data perspective generation and visualization. Some example embodiments provide advantages of exploring various ideas, represented by and/or associated with one or more perspective data objects, without utilizing a complex re-configuration stage for one or more machine learning models, and/or without utilizing one or more team members conventionally required to ensure proper implementation of the idea(s). Similar advantages are obtained when desiring to change an existing idea, change the level of granularity associated with the processing, and/or the like. Some embodiments are configured to cause rendering of interfaces associated with the processing, and/or enable a user to user interaction for inputting the desired level of granularity of the generated perspectives and/or desired granularity for the analysis of the data set. Some embodiments generate various graphical data objects to enable such processing.