Intent-Graph Network Query Mechanism for Cross-Database Retrieval
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Solution Overview
Problem
Existing network management systems require manual stitching of data from multiple databases, leading to inefficient use of computing power, memory, and network bandwidth, and necessitate bespoke code for filtering, sorting, and joining data, which complicates the response to queries and prolongs issue diagnosis.
Innovation Solution
A query mechanism that automatically stitches data from different databases together, providing a turn-key solution for network administrators, reducing the need for manual coding and optimizing resource usage by implementing an intent graph-based system to select and retrieve data from network devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If manual stitching of data from multiple databases is performed, then data can be retrieved from different sources, but computing power, memory, and network bandwidth are used inefficiently
Solution Approach 1:
The patent introduces an intermediary layer (query mechanism/abstraction layer) between the user and multiple databases. This intermediary automatically manages data retrieval, filtering, sorting, and joining operations, eliminating the need for manual stitching while optimizing resource usage. The intermediary translates high-level queries into efficient database operations, reducing both computing power waste and retrieval time.
2Adaptability or versatility
If bespoke code is written for filtering, sorting, and joining data, then custom data processing can be achieved, but the complexity of the system increases
Solution Approach 1:
The patent implements a universal query mechanism that handles filtering, sorting, joining, and other data processing operations through a single standardized interface. This multi-functional system replaces the need for multiple bespoke code implementations, maintaining data processing flexibility while significantly reducing system complexity. The query mechanism serves as a universal tool that adapts to different data processing needs without requiring custom code for each scenario.
3Ease of operation
If data is manually stitched together to monitor a network, then network monitoring can be performed, but the time to diagnose and resolve issues increases
Solution Approach 1:
The patent implements preliminary action by pre-establishing the query mechanism and data retrieval pathways before actual network monitoring begins. The system is pre-configured with knowledge of data locations, relationships, and retrieval methods across multiple databases. When network issues arise, the pre-established query mechanism can immediately retrieve and correlate relevant data without manual intervention, significantly reducing issue diagnosis time while maintaining ease of operation.
4Loss of information
If a large amount of data is transferred from multiple databases, then complete information can be obtained, but network bandwidth is used sub-optimally
Solution Approach 1:
The patent applies the extraction principle by selectively retrieving only the specific data needed to answer each query, rather than transferring large amounts of unnecessary data. The query mechanism extracts precise information from multiple databases based on query parameters, filters out irrelevant data, and returns only the essential information. This approach maintains data completeness for the specific query while dramatically reducing network bandwidth consumption by avoiding transfer of extraneous data.
Data Source
AI summary
A system comprising one or more processors configured to receive a query indicating one or more of filtering information, sorting information, or joining information and retrieve, from a first datastore, an intent graph for a network, wherein the intent graph comprises nodes representing components of the network and edges representing connections between the components. The one or more processors being further configured to select a subset of a plurality of network devices of the network based on the query and the intent graph retrieved from the first datastore and retrieve, from a second datastore, data received from the plurality of network devices of the network. The one or more processors being further configured to determine a response to the query based on the selected subset of the plurality of network devices and the data retrieved from the second datastore and output the response to the query.