Data Aggregation Layer for ML-Based Network Performance Visibility
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Solution Overview
Problem
In networked environments, managing data across multiple databases with disparate specifications makes it difficult for administrators to gain a holistic view of application performance, leading to undetected issues and increased manual effort in data retrieval.
Innovation Solution
A service aggregates data from multiple sources using machine learning (ML) models, transforming and formatting it for input into specific models, and generates visualizations for quick issue detection and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored across multiple databases with disparate specifications, then data can be maintained according to different application requirements, but it becomes difficult to gain holistic visibility into network performance
Solution Approach 1:
The patent introduces a data aggregation service as an intermediary layer between multiple databases and the network administrator. This service retrieves data from various databases with different specifications, transforms and standardizes the data formats, and presents unified information through a single interface. This mediator resolves the contradiction by maintaining database-specific storage flexibility while providing holistic network visibility through standardized data aggregation and presentation.
2Ease of manufacture
If administrators access individual databases to retrieve data, then data can be retrieved according to specific database specifications, but manual effort and time increase significantly
Solution Approach 1:
The data aggregation service operates autonomously to retrieve, transform, and standardize data from multiple databases without requiring manual intervention from administrators. The service self-manages the complex tasks of navigating different database specifications, querying appropriate data, converting formats, and assembling comprehensive reports. This self-service mechanism maintains data retrieval accuracy while dramatically reducing the time and effort administrators must invest.
Solution Approach 2:
The system performs preliminary data retrieval and transformation actions automatically before administrators need the information. The aggregation service proactively collects data from various databases, pre-processes and standardizes the formats, and prepares comprehensive reports in advance. This preliminary action eliminates the need for administrators to manually query multiple databases, significantly reducing their time investment while maintaining accurate data retrieval.
3Quantity of substance
If data is retrieved from multiple databases, then comprehensive information can be gathered, but data formatting and specification differences make immediate use difficult
Solution Approach 1:
The patent applies parameter changes by transforming data from various formats and specifications into a unified standardized format. The aggregation service modifies data parameters such as field names, data types, and structure to conform to a common schema that is easily consumable by administrators and downstream systems. This parameter transformation maintains data completeness while significantly improving ease of operation and immediate usability.
4Ease of operation
If manual data retrieval is performed across databases, then specific data can be accessed, but issues affecting multiple applications remain undetected
Solution Approach 1:
The data aggregation service merges data from multiple databases into a single unified view that presents comprehensive information about network performance across all applications. By combining data that would otherwise be scattered across different database interfaces, the service enables administrators to detect performance issues affecting multiple applications simultaneously. This merging maintains simple data access while dramatically improving reliability through comprehensive visibility.
Data Source
AI summary
Presented herein are systems and methods for aggregating data from disparate sources to output information. A computing system may transform a first plurality of datasets of a plurality of data sources by converting a first format of the corresponding data source for each of the first plurality of datasets to generate a second plurality of datasets in a second format of the computing system. The computing system may identify, from the second plurality of datasets, a subset of datasets using a feature selected for evaluation of a utility of the feature. The computing system may apply a machine learning model configured for the selected feature to the subset of datasets to generate an output that measures a likelihood of usefulness. The computing system may cause a visualization of the output for the feature to be displayed for presentation on a dashboard interface based on a template configured for the feature.