Engramic Indexing for IT Infrastructure Query Overhead
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Solution Overview
Problem
Current enterprise-scale information technology infrastructure management and search processes are inefficient due to extensive overhead in managing multiple datacenters, which hampers query processing and resource retrieval.
Innovation Solution
The implementation of an engramic indexing service that uses semantic context and natural language processing to optimize query context through mapped semantics and machine learning, enabling efficient access and management of IT infrastructure by generating query semantic fingerprints and computing similarity metrics to identify relevant datacenters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search and management methods are used for multiple datacenters, then comprehensive resource access is achieved, but extensive overhead and inefficiency occur
Solution Approach 1:
The patent creates semantic fingerprints as simplified copies of complex datacenter profiles and query intents. Instead of managing full datacenter metadata and performing complex semantic matching, the system uses compact fingerprint representations that capture essential semantic characteristics, dramatically reducing management overhead while maintaining matching accuracy
Solution Approach 2:
The patent transforms the query matching problem from complex semantic analysis to simple numerical comparison by converting semantic concepts into fingerprint vectors. This parameter transformation allows efficient similarity computation using mathematical operations rather than complex linguistic processing, improving productivity while reducing operational complexity
2Loss of time
If semantic fingerprinting and similarity computation are implemented, then query efficiency is improved, but computational overhead increases
Solution Approach 1:
The patent computes similarity metrics only between the query fingerprint and relevant datacenter fingerprints rather than performing exhaustive semantic analysis across all datacenters. This partial action approach reduces computational overhead by focusing processing only on necessary comparisons, thereby reducing energy consumption while maintaining fast response times
Solution Approach 2:
The patent pre-computes and stores semantic fingerprints for all datacenters before query processing. This preliminary action eliminates the need for complex semantic analysis during query execution, shifting computational work to an offline phase and enabling rapid online query processing with minimal energy consumption
Data Source
AI summary
A method and system for engramic indexing of information technology (IT) infrastructure. Specifically, the method and system disclosed herein enable the efficient access, search, and/or management of enterprise-scale IT infrastructure and topologies using semantic context and natural language processing. That is, query context may be optimized using mapped semantics based on organizational constructs and machine learning, thereby reducing query overhead, increasing response performance, and improving contextual display capabilities in mass-scale environments.