Object Model Data Mapping for Heterogeneous Sources
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Solution Overview
Problem
The complexity and variability of business data processes lead to scattered and unstructured data, making large-scale analysis difficult and costly, as businesses struggle to integrate data from multiple databases with different formats and relationships.
Innovation Solution
A machine-based method that records object classes of an object model, maps data from multiple data sources with different formats to these classes, and produces comparable mapped data, allowing for structured data storage and analysis through object representation and context modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple databases with different formats and relationships is integrated using traditional methods, then data completeness is improved, but device complexity and analysis cost increase significantly
Solution Approach 1:
The patent introduces an object model as an intermediary layer between multiple data sources with different formats. This object model provides a standardized representation that mediates the integration process, allowing data from diverse sources to be mapped to common object classes without requiring complex direct integrations between all data sources, thereby reducing system complexity while maintaining data completeness.
Solution Approach 2:
The patent transforms data from multiple formats by changing their parameters to fit a unified object model. Data formats, schemas, and structures are converted into standardized object representations with consistent attributes and relationships, enabling integrated analysis without the complexity of handling multiple proprietary formats simultaneously.
2Loss of information
If traditional relational tables with various cardinalities are used to represent data relationships, then data richness is improved, but measurement precision and analysis difficulty increase
Solution Approach 1:
The patent segments complex relational data structures into discrete object classes with well-defined attributes and relationships. Instead of using monolithic relational tables with complex cardinalities, the data is divided into modular objects that can be independently analyzed and recombined, improving measurement precision while preserving data richness.
Solution Approach 2:
The patent transitions from traditional two-dimensional relational tables to a multi-dimensional object model where data relationships are expressed through object hierarchies, associations, and compositions. This dimensional change allows for more precise representation of complex relationships without the analytical difficulties of high-cardinality joins.
3Quantity of substance
If massive flat databases are used to store integrated data, then data capacity is improved, but ease of operation and analysis efficiency deteriorate
Solution Approach 1:
The patent segments large volumes of integrated data into organized object classes and instances, replacing massive flat databases with structured object-oriented data models. This segmentation allows for efficient querying and analysis by enabling targeted access to specific object types and their relationships without scanning entire flat tables, thereby improving ease of operation while maintaining data capacity.
Data Source
AI summary
Among other things, a machine-based method is described. The method comprises recording object classes of an object model, producing an object representation for data of two or more data sources based on a mapping of data formats of the data sources to the object classes of the object model, and producing mapped data from the data sources. The mapped data is available in objects of the object classes and is comparable in the object representation. At least two of the data sources have different data formats.