Predictive Data Schemas for Selective Ingestion and Indexing

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Solution Overview

Problem

Traditional data management systems struggle to efficiently manage large and complex datasets due to resource-intensive processing and storage requirements, inefficiencies in data indexing, and the inability to adapt to changing data types and use cases, particularly in cybersecurity applications.

Innovation Solution

Implement a semantic layer using predictive modeling and large language models to dynamically ingest and index data based on actual use cases, allowing for selective data processing and indexing, reducing resource usage and enhancing scalability.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If all data is indexed to enable comprehensive search and analysis, then search completeness is improved, but processing time and storage resources increase significantly

Engineering Contradiction:

Improvesearch completenessVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by using predictive modeling to identify and index only the data subsets that are likely to be needed for future searches. This allows the system to prepare relevant data in advance without indexing all available data, thus balancing search completeness with processing efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of indexing all data (excessive action) or no data (insufficient action), the system applies partial action by indexing only the optimal subset of data predicted to be relevant. This selective indexing approach achieves sufficient search completeness while minimizing processing time and resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

2Productivity

If manual early-binding schemes are created to organize data based on predefined rules, then data retrieval efficiency is improved, but system adaptability to changing data types deteriorates

Engineering Contradiction:

Improvedata retrieval efficiencyVSAvoidsystem adaptability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system transitions from static manual early-binding schemes to dynamic predictive modeling that automatically adapts to changing data types and search patterns. The binding scheme evolves based on predicted future needs, maintaining both retrieval efficiency and adaptability through continuous learning and adjustment.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs self-service by automatically generating and updating binding schemes without manual intervention. The predictive model autonomously identifies data organization patterns and creates efficient retrieval structures, eliminating the need for manual scheme creation while maintaining adaptability to changing requirements.

Inventive Principle:

Principle #25Self-service

3Power

If increasing compute capabilities are provided to process larger data volumes, then data processing capacity is improved, but resource costs increase

Engineering Contradiction:

Improvedata processing capacityVSAvoidresource costs

Core Design Contradiction:

PowerVSUse of energy by moving object

Solution Approach 1:

The system extracts and processes only the essential subsets of data that are predicted to be relevant, rather than processing all available data. This extraction approach maintains adequate processing capacity for critical operations while significantly reducing the compute resources and energy costs required.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes the parameter of data volume processed by using predictive modeling to determine optimal subset sizes. This dynamic parameter adjustment allows the system to maintain processing capacity for high-priority operations while reducing overall resource consumption by processing smaller, more targeted data subsets.

Inventive Principle:

Principle #35Parameter changes

4Speed

If data is processed into searchable format before storage, then search efficiency is improved, but processing overhead increases

Engineering Contradiction:

Improvesearch efficiencyVSAvoidprocessing overhead

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The system applies preliminary action by processing and indexing data subsets predictively before they are needed for searches. This allows search-efficient data formats to be prepared in advance for anticipated queries, reducing the processing overhead at search time while maintaining high search efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial action by processing only the necessary subset of data into searchable format, rather than converting all data. This selective processing achieves sufficient search efficiency for relevant data while minimizing the processing overhead associated with format conversion.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250292115A1Systems and methods for generation and use of dynamic ingestion pipelines and associated data schemas for indexing of data using predictive modeling

Publication Date: 2025.09.18 ZIGGURATUM INC

AI summary

Embodiments of systems and methods for generation and validation of schemas for data sources configured for a data management system using predictive models are disclosed herein. Such systems and methods may generate and validate a schema using a predictive model based on examples of data from that data source.