Universal Data Representation for Searchable Heterogeneous Metadata
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Solution Overview
Problem
The heterogeneity of data poses challenges in achieving insights or machine learning models due to different data representations, layouts, and semantics, complicating data processing and management.
Innovation Solution
A universal data representation (UDR) system that extracts metadata from heterogeneous data and arranges it into a common schema, enabling consistent data management and searchability across various data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heterogeneous data with different representations, layouts, and semantics is processed directly, then data diversity and information richness are maintained, but data processing complexity increases significantly
Solution Approach 1:
The patent introduces a standardized data representation format as an intermediary layer between heterogeneous data sources and processing systems. This intermediate format includes standardized fields for data identification, structure, semantics, and relationships, allowing diverse data to be processed uniformly without losing information richness.
Solution Approach 2:
The patent segments heterogeneous data into standardized components including data identification elements, structural elements, semantic elements, and relationship elements. Each component is processed and standardized separately, then reassembled into a unified representation that maintains data diversity while reducing processing complexity.
2Reliability
If data is stored in its original heterogeneous formats, then data fidelity and source-specific characteristics are preserved, but data searchability and accessibility across different data types deteriorate
Solution Approach 1:
The standardized data representation serves as a mediator that preserves source data fidelity through standardized reference fields while enabling uniform search operations. The standardization layer includes fields that reference original data sources and maintain source-specific characteristics while allowing consistent querying across all data types.
Solution Approach 2:
The patent creates a universal data representation format that can represent multiple data types (images, text, audio, video, structured data) through a single standardized structure. This universal format enables the same search and access operations to work across all data types while maintaining the unique characteristics of each source format.
3Adaptability or versatility
If custom data formats are used for different applications and users, then application-specific requirements are met, but data management and preprocessing complexity increases
Solution Approach 1:
The standardized data representation format is designed to be universally applicable across different applications and users while accommodating application-specific requirements through configurable fields. The standard structure includes extensible elements that allow custom data types and relationships to be defined without creating separate processing pipelines for each application.
Solution Approach 2:
The patent allows application-specific parameters and configurations to be defined within the standardized framework. Different applications can specify different subsets of standardized fields, different validation rules, and different processing priorities without changing the fundamental data representation structure, thereby reducing management complexity.
Data Source
AI summary
This disclosure provides methods, devices, and systems for metadata extraction. The present implementations more specifically relate to a universal data representation (UDR) for heterogeneous data. As used herein, the term “UDR” refers to a metadata format that can be used to represent source data from various source data repositories and/or source content types. More specifically, metadata can be extracted from various content items and stored in respective UDR documents that describe heterogenous data in a common format. In other words, UDR documents share a common schema regardless of the schema or format of the source content. For example, a UDR data structure for a text document can have the same (or substantially similar) format as a UDR data structure for a relational database. Accordingly, UDR can significantly reduce data processing complexity by reducing the number of disparate data representations that must be understood by a data processing pipeline and/or application.