Big Data Model Decoupling Properties and Metadata
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Solution Overview
Problem
Conventional techniques are inadequate for analyzing and modeling Big Data due to its sheer size, volume, and complex analytical requirements, which exceed the capabilities of typical software tools, and fail to accommodate the properties that define Big Data effectively.
Innovation Solution
A Big Data model is introduced that decouples datasets into properties and metadata, enabling processing and analysis by representing elements as properties rather than attributes, and utilizing metadata for calculating summary information, thereby accommodating the complexity and size of Big Data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional software tools are used to analyze Big Data, then the analysis process is simple, but the tools cannot handle the size and complexity of Big Data
Solution Approach 1:
The patent segments the Big Data analysis system into multiple specialized components: a data ingestion layer for capturing data from various sources, a data modeling layer for creating analytical representations, a metadata management layer for tracking data properties, and an analysis execution layer. This segmentation allows each component to handle specific aspects of Big Data processing independently, making the overall system manageable despite the large data volumes.
Solution Approach 2:
The patent introduces a data modeling layer as an intermediary between the raw Big Data and the analysis tools. This layer creates analytical models that represent the essential characteristics of the data without requiring tools to process the entire raw dataset. The metadata management layer serves as another intermediary, providing structured information about data properties that enables efficient query processing without examining the actual data contents.
2Loss of information
If Big Data is processed as a whole, then complete analysis is achieved, but processing time and resource requirements increase significantly
Solution Approach 1:
The patent extracts essential characteristics of Big Data into metadata representations. Instead of processing entire datasets, the system extracts key properties such as data types, relationships, constraints, and statistical summaries into metadata models. This extraction enables analysis operations to work with compact metadata representations rather than the full voluminous datasets, dramatically reducing processing time while preserving analytical completeness.
Solution Approach 2:
The patent performs preliminary actions by creating data models and metadata structures before actual analysis execution. The system pre-processes data to establish analytical models, define relationships, and compute summary statistics in advance. This preliminary modeling enables subsequent analysis operations to proceed efficiently by leveraging pre-computed information rather than processing raw data from scratch each time.
3Measurement precision
If detailed properties of Big Data are maintained, then analysis precision is improved, but storage and processing overhead increases
Solution Approach 1:
The patent creates simplified copies of Big Data in the form of analytical models and metadata representations. Instead of storing and processing all original data details, the system maintains copied representations that capture essential analytical properties. These models include structured information about data relationships, constraints, and characteristics that preserve analysis precision while occupying minimal storage space compared to the original voluminous datasets.
Data Source
AI summary
A method, apparatus, and computer implemented method for analyzing a Big Data dataset, the method comprising performing analysis on a big data dataset by applying a set of analytical tool to a Big Data Model; wherein the Big Data Model decouples the Big Data dataset into properties and metadata; wherein each of the properties represent part of the Big Data dataset to enable processing and analysis; wherein the metadata enables calculation of summary information for the Big Data dataset.