Universal Data Transformation Framework for Heterogeneous Sources
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Solution Overview
Problem
The challenge lies in efficiently transforming disparate data sources with different formats and schemas into a unified format for analysis, as existing methods are laborious and difficult to optimize, especially when adding new data sources requires new extraction and processing efforts.
Innovation Solution
A method and apparatus that transform data from multiple sources by selecting relevant fields, expanding them into a uniform format with identical columns, allowing for efficient combination and analysis, enabling analysts to work with normalized data sets without dealing with individual source nuances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data extraction is performed from each source with customized processing, then data can be analyzed from individual sources, but the process becomes laborious and difficult to optimize
Solution Approach 1:
The patent implements a universal data extraction framework that can handle multiple data sources with different formats and schemas through a single standardized interface. The system defines a common data model with standardized fields (e.g., timestamp, location, device_id) that all data sources map to, eliminating the need for customized extraction logic for each source while maintaining the ability to accurately analyze data from individual sources.
Solution Approach 2:
The system transforms data from various sources by changing their parameter representations to match a universal schema. Configuration files or metadata define the mapping between source-specific parameters and standardized parameters, allowing the extraction process to adapt to different data formats without requiring custom processing logic for each source.
2Adaptability or versatility
If new data sources are added with different formats, then more comprehensive analysis is possible, but new extraction and processing efforts are required
Solution Approach 1:
The patent creates a universal extraction framework that can accommodate new data sources without requiring changes to the core extraction logic. The system uses configuration-driven mappings that define how new data sources should be transformed into the universal format, allowing new sources to be added by simply defining their mapping rules rather than creating new extraction processes.
Solution Approach 2:
The system introduces an intermediary layer (configuration metadata or mapping files) that sits between the diverse data sources and the extraction logic. This intermediary defines the transformation rules from source-specific formats to the universal format, allowing new data sources to be integrated through configuration rather than code changes, thus reducing complexity.
3Reliability
If data is stored in source-specific formats, then data integrity is maintained, but storage, retrieval, and indexing performance deteriorates
Solution Approach 1:
The patent performs data transformation into a universal format during the extraction phase rather than during queries or analysis. By pre-processing the data to convert it into a standardized format with consistent field names, types, and structures, the system maintains data integrity from the original sources while optimizing storage and retrieval performance for subsequent operations.
Solution Approach 2:
The system changes the parameter representation of data from source-specific formats to a universal format during extraction. This transformation standardizes field names, data types, and structures, enabling efficient storage, retrieval, and indexing operations while preserving the original data values and meaning through the mapping process.
4Measurement precision
If customized processing is applied to each data set, then analysis accuracy is improved, but the overall analysis process becomes more difficult and time-consuming
Solution Approach 1:
The patent implements a universal data model that enables a single standardized analysis process to work across all data sources. By transforming all data into a common format with consistent fields and structures, the system allows analysts to apply the same analysis logic to all datasets without requiring customized processing for each source, significantly reducing analysis time while maintaining accuracy through the fidelity of the transformation mappings.
Data Source
AI summary
One or more processing devices receive data from a plurality of data sources, wherein the plurality of data sources have different data formats. The plurality of data sources are transformed into a universal data format, and using a second data source, the transformed data is enriched by applying one or more rules against the transformed data and the second data source, resulting in at least one of a first value substituted for a missing value, or the first value substituted for a second value, wherein the first value and the second value are obtained from different ones of the plurality of data sources and the second data source.