Recursive Data Discovery System with Experiential Feedback Loops
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Solution Overview
Problem
Current data discovery products are limited in analyzing and disambiguating inquiry data, accessing and utilizing data sources, maintaining metadata, and applying experiential learning for recursive searches, leading to inefficient data retrieval and decision-making processes.
Innovation Solution
An automated recursive discovery process that utilizes results from one inquiry to initiate subsequent searches across multiple data sources, generating and curating experiential information to influence future data access, and applying business rules for consistent decision-making, while being language-agnostic and context-independent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current discovery products provide data directly to end-users without storing information, then data access is simple, but the system cannot perform recursive learning or improve future searches
Solution Approach 1:
The system implements feedback loops where discovered data and metadata are stored and fed back into subsequent searches. The processor uses results from one inquiry to initiate and refine future inquiries, creating a self-improving system that learns from each search operation to enhance future data discovery capabilities.
Solution Approach 2:
The system performs self-service by automatically using its own discovered data and metadata to improve its search capabilities. The processor recursively uses experiential information about data veracity, provenance, and content to automatically refine search strategies without requiring external intervention or reconfiguration.
2Reliability
If the system stores metadata about discovered data and sources, then recursive learning is enabled, but data processing complexity increases
Solution Approach 1:
The system segments metadata into distinct categories including data veracity, provenance, content characteristics, and source attributes. This segmentation allows the processor to selectively apply different analysis methods to different metadata types, improving reliability without overwhelming processing complexity.
Solution Approach 2:
The metadata structure is designed to be universal and multi-functional, serving multiple purposes: quality assessment, search refinement, source evaluation, and recursive learning. This universal metadata framework eliminates the need for separate processing systems for different data quality aspects.
3Manufacturing precision
If the system performs comprehensive data analysis and curation, then data quality improves, but processing time increases
Solution Approach 1:
The system applies partial action by performing data analysis and curation selectively based on search context and data importance. Not all discovered data undergoes full curation processing - the system intelligently applies appropriate levels of analysis based on data relevance, maintaining quality while reducing unnecessary processing time.
Solution Approach 2:
The system performs preliminary action by pre-processing and categorizing metadata during the discovery phase, preparing data for future use. This preliminary organization of experiential information about data sources and content enables faster subsequent searches without requiring comprehensive re-analysis.
4Productivity
If the system uses recursive processes to learn from each search, then future search effectiveness improves, but computational resources increase
Solution Approach 1:
The system discards redundant or low-value discovered data while recovering and retaining high-value experiential information about data sources, veracity patterns, and effective search strategies. This selective retention reduces computational overhead while maintaining the benefits of recursive learning for future searches.
Data Source
AI summary
A system and a method used for data discovery in accordance with an inquiry in which multiple sources, which may be web sites or other data sources, are examined for data relevant to the inquiry. The process and method is performed recursively an indeterminate number of iterations, using data and metadata from multiple sources to corroborate discovered data and metadata from other sources, until no further relevant data or sources are found, or adjudication or exception rules have been met. Discovered data and metadata are curated, adjudicated to assess reliability, synthesized, and clustered into composite records using precedence rules and provenance to determine the most reliable data sources as well as terms of use for each source. Data, metadata, and information about each search are retained and can be used for subsequent purposes, such as subsequent searches or other downstream activities.