Big Data Model Using Non-Specific Representations
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Solution Overview
Problem
Conventional techniques are inadequate for modeling and analyzing Big Data due to its large size, volume, and complex analytical requirements, which exceed the capabilities of typical software tools, and fail to accommodate the unique properties of Big Data sets such as those from digital pathology, seismological surveys, or financial industries.
Innovation Solution
A Big Data model is developed that represents dataset elements and relationships using computer-implemented objects, incorporating meta-information and properties like analytical, size, and structural aspects, allowing for semantic and unstructured analysis, and optimizing resource allocation based on analysis types, enabling efficient storage and processing of large datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional software tools are used to model and analyze data, then ease of operation is maintained, but the ability to handle Big Data with large size and complex analytical requirements deteriorates
Solution Approach 1:
The patent segments the Big Data model into distinct representations: non-specific representations for the dataset itself (DataSet representation), and separate non-specific representations for properties (Properties representation). This segmentation allows each component to be independently managed and optimized, enabling the system to handle Big Data capabilities while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces non-specific representations as intermediary objects that mediate between the Big Data and the analysis operations. These representations include meta-information about the data, its properties, and relationships, serving as an intermediary layer that enables conventional tools to effectively handle Big Data without requiring fundamental changes to the tools themselves.
2Measurement precision
If detailed analysis of Big Data is performed, then measurement precision and analytical depth are improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by creating non-specific representations that capture meta-information and properties of the Big Data before actual analysis is performed. This includes pre-establishing data relationships, structural characteristics, and property descriptions, which enables faster and more precise analysis during the actual processing phase without requiring extensive computational resources.
Solution Approach 2:
The patent extracts essential characteristics from the Big Data into separate non-specific representations. By taking out meta-information, property descriptions, and structural details into dedicated representations, the system enables detailed analysis of specific aspects without processing the entire Big Data dataset, thereby improving measurement precision while reducing processing time.
3Quantity of substance
If Big Data is stored and processed using conventional methods, then ease of operation is maintained, but storage efficiency and resource optimization deteriorate
Solution Approach 1:
The patent creates a universal Big Data model that can handle multiple types of data and analysis operations through a single integrated framework. The non-specific representations and property descriptions serve multiple functions: data organization, metadata management, property characterization, and analysis guidance, thereby improving storage efficiency without requiring separate systems for each function.
Solution Approach 2:
The patent implements a nested structure where non-specific representations contain meta-information about properties, which in turn contain descriptions of data characteristics. This nested organization allows efficient storage by organizing information hierarchically, where only necessary details are stored at each level, reducing overall storage requirements while maintaining system manageability.
Data Source
AI summary
A method, apparatus, and computer implemented method for modeling a Big Data dataset, the method comprising creating non-specific representations of the Big Data dataset by representing, as objects in a computer model, non-specific representations including metaInformation, DataSet, BigData and Properties representations and creating non-specific representations of Properties, wherein at least one of the representations are selected from the group consisting of Analytical, size, volume, and structural.