Centralized Data Platform for Industrial AI Training
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Solution Overview
Problem
Current data management systems lack a unified strategy for data integration, management, and distribution, leading to isolated digital data silos that hinder the development of industrial AI applications.
Innovation Solution
A procedure and infrastructure for providing training data for computer-aided applications, involving the receipt, homogenization, and storage of raw data in a database, with integrated source code management for reproducibility and traceability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in distributed local servers and databases across multiple organizations, then data ownership and security are maintained, but data accessibility and integration capability deteriorate
Solution Approach 1:
The patent introduces a centralized data platform as an intermediary layer between distributed data sources and AI applications. This platform provides unified data access, homogenization services, and integration capabilities while preserving the distributed storage architecture. The intermediary enables data to be accessed and integrated centrally without requiring data to physically move from its secure location, thus maintaining both security and accessibility.
2Ease of operation
If data is homogenized and consolidated in a centralized database, then data integration and accessibility are improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The patent segments the data management system into distinct functional layers: data collection layer, homogenization layer, storage layer, and access layer. Each layer handles specific tasks independently, allowing the system to manage complexity through modular architecture. The homogenization layer processes data from multiple sources using standardized formats, while the storage layer manages consolidated data, enabling independent optimization of each component.
3Adaptability or versatility
If data engineers develop custom methods and tools for each data source, then data processing flexibility is improved, but development time and resource requirements increase
Solution Approach 1:
The patent implements a universal data platform that handles multiple data sources, formats, and processing requirements through a single integrated system. The platform provides standardized interfaces and homogenization capabilities that work across diverse data types, eliminating the need to develop separate custom solutions for each data source. This multi-functional approach maintains flexibility while significantly reducing development time and resource requirements.
Data Source
Figure 1~2
AI summary
The invention relates to a method for providing training data (32) for a computer-based application (34, 36) by means of an electronic computing device (10), comprising the steps of: (S1) receiving raw data (12, 14) from at least one data owner (16, 18) by means of a receiving device (28) of the electronic computing device (10); (S3) homogenizing the received raw data (12, 14) by means of the electronic computing device (10); (S4) storing the homogenized data (22) in a database (24) of the electronic computing device (10); (S5) and providing the homogenized data (22) as training data (32) for retrieval by a computer-based application (34, 36) by means of the electronic computing device (10). The invention further relates to a computer program product, a computer-readable storage medium, and an electronic computing device (10).