Semantic-Layer Predictive Indexing for Data Management

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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, scalability issues, and the inability to adapt to changing data types and use cases, particularly in contexts like cybersecurity, where indexing and structuring data manually is time-consuming and inefficient.

Innovation Solution

Implementing a semantic layer using predictive modeling and large language models to contextualize data during ingestion, allowing selective indexing and structuring based on actual use cases, reducing resource usage and enabling dynamic adaptation to data changes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If all data is indexed to enable search and analysis, then data accessibility is improved, but processing costs and resource consumption increase exponentially

Engineering Contradiction:

Improvedata accessibilityVSAvoidprocessing costs

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by using predictive models to identify and index only the data subsets that are likely to be needed for future queries. Instead of indexing all data upfront or waiting for actual queries, the predictive model proactively determines which data should be indexed in advance, balancing accessibility with resource efficiency.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial action by indexing only a subset of data rather than all data. The predictive model determines the optimal subset to index based on predicted query patterns, avoiding the excessive resource consumption of full data indexing while maintaining adequate data accessibility for anticipated use cases.

Inventive Principle:

Principle #16Partial or excessive action

2Productivity

If manual schema creation and data structuring is performed to organize data efficiently, then data retrieval efficiency is improved, but time and labor requirements increase significantly

Engineering Contradiction:

Improvedata retrieval efficiencyVSAvoidschema creation time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service by using predictive models and large language models to automatically generate schemas and structure data without manual intervention. The predictive model analyzes data characteristics and query patterns to autonomously create optimized data structures, eliminating the time-consuming manual schema creation process while maintaining high retrieval efficiency.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces the mechanical manual process of schema creation with an automated intelligent system. Large language models and predictive algorithms substitute human experts in analyzing data requirements and designing schemas, transforming a manual labor-intensive task into an automated computational process that is both faster and scalable.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If data is processed into searchable format using traditional methods, then query capability is improved, but processing time and computational resources increase

Engineering Contradiction:

Improvequery capabilityVSAvoidprocessing time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system performs preliminary processing of data into searchable formats only for the subsets identified by the predictive model as likely to be queried. This avoids the time-consuming full data processing while maintaining query capability for predicted use cases, reducing overall processing time significantly.

Inventive Principle:

Principle #10Preliminary action

4Power

If increasing compute capabilities are provided to handle larger datasets, then data processing capacity is improved, but infrastructure costs and resource requirements increase

Engineering Contradiction:

Improvedata processing capacityVSAvoidinfrastructure costs

Core Design Contradiction:

PowerVSUse of energy by moving object

Solution Approach 1:

The system uses partial action by processing and indexing only the necessary subset of data determined by predictive models. This approach maintains adequate data processing capacity for actual query needs while avoiding the excessive infrastructure costs associated with processing and storing indexes for all possible data, achieving cost-effective scaling.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250291778A1Systems and methods for predictively indexing data in a data management system utilizing a predictive model

Publication Date: 2025.09.18 ZIGGURATUM INC

AI summary

Embodiments may index or structure data and associated schemas for data management based on statistical modeling, machine learning, or other models based on user behavior and application dependencies or usage. In embodiments of this approach, sets of data that may be needed may be predicted, and these set of data indexed for different aspects or functionality of as required.