AI Sandbox Using Metadata Graphs for Siloed Data Pipelines
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Solution Overview
Problem
Existing systems require significant computational resources and manual intervention to access siloed data across disparate locations, leading to inefficiencies, data integrity issues, and wasted memory due to duplicate data storage, making it difficult to scale and integrate new applications.
Innovation Solution
A unified metadata graph system that uses natural language processing and large language models to traverse data silos, reducing the need for new data silos and reconfiguration by providing a centralized access point through a graphical user interface, leveraging domain-specific ontologies to optimize metadata generation and reduce retrieval times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data silos are created for each computing system to process data efficiently, then data processing efficiency is improved, but compute capacity requirements increase due to duplicate data storage and maintenance
Solution Approach 1:
A unified metadata graph is introduced as an intermediary layer between computing systems and data silos. This metadata graph serves as a virtual catalog that maps data across different silos without requiring physical data duplication or movement, enabling efficient data location and access while eliminating redundant storage and associated compute resources.
Solution Approach 2:
The unified metadata graph provides universal access to data across multiple computing systems and data silos through a single interface. Instead of requiring separate data access mechanisms for each silo, the metadata graph serves all computing systems with a common data discovery and access mechanism, reducing overall system complexity and resource consumption.
2Reliability
If data silos are created with unique variable names and integrity constraints for each computing system, then data integrity is maintained, but data redundancy increases causing wasted compute resources
Solution Approach 1:
Instead of copying physical data across multiple silos, the system creates copies of metadata descriptions that map to the same underlying data. The unified metadata graph contains virtual references to data located in different silos, allowing multiple computing systems to access the same physical data without duplication, thereby eliminating wasted compute resources while maintaining data integrity through the original silos.
Solution Approach 2:
The metadata graph acts as an intermediary layer that preserves the unique variable names and integrity constraints of individual data silos while providing a unified view. This intermediary maintains data integrity by referencing original data structures without requiring changes to the underlying silos, simultaneously eliminating redundancy.
3Loss of substance
If a coordinated and consolidated silo is built to replace all existing silos, then data redundancy is eliminated, but the effort and complexity to build and verify increases significantly
Solution Approach 1:
The system segments the data access problem into two independent parts: the existing data silos remain unchanged as storage backends, while a separate unified metadata graph is constructed as a virtual layer. This segmentation allows the metadata graph to be built independently without requiring changes to existing silos, dramatically reducing integration complexity while still eliminating data redundancy through virtual unification.
Solution Approach 2:
By introducing the unified metadata graph as an intermediary layer, the system avoids the complex task of consolidating and replacing existing silos. The metadata graph mediates between data consumers and the distributed silos, providing unified access without requiring physical consolidation, thereby eliminating redundancy while minimizing integration complexity.
4Measurement precision
If manual intervention is used to access siloed data across disparate locations, then data access precision can be maintained, but time consumption and operational complexity increase
Solution Approach 1:
The unified metadata graph enables self-service data access by automatically resolving data locations across silos. When a data request is made, the metadata graph autonomously traverses the graph structure to locate the required data in appropriate silos without manual intervention, maintaining precise data access while significantly reducing time consumption through automated resolution.
Solution Approach 2:
The manual mechanical process of searching through multiple silos is replaced by an automated computational traversal of the metadata graph. The system uses algorithmic graph traversal and metadata resolution to automatically locate data, substituting manual search operations with automated computational processes that are both faster and equally precise.
Data Source
AI summary
A system facilitates a process for automatically generating artificial intelligence (AI) models. The system receives a first natural language input from a user that includes a set of phrases and an instruction to analyze data associated with the set of phrases using an AI model. The system accesses a metadata graph to determine a node corresponding to the set of phrases, where nodes in the metadata graph indicate internal data objects stored in data silos. The system processes the internal data objects indicated by the determined node to generate a first set of application data. The AI model is applied to the first set of application data to generate one or more outputs. Additional user inputs can be received to modify the first set of application data and apply the AI model to the modified data, until a desired data pipeline for the model has been constructed.