Centralized Database Anomaly Detection for Remote Systems
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Solution Overview
Problem
Current tools lack the capability to detect hardware and software anomalies in remote systems, limiting proactive responses to issues and failing to provide comprehensive support for complex information handling systems.
Innovation Solution
A method and system for aggregating and analyzing data from remote systems' subcomponents in a centralized database, using an electronic database server and client to identify anomalies by comparing statistics counters and applying defined rules, enabling automated detection of hardware and software issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data aggregation and automated analysis are implemented, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces a centralized database server as an intermediary component that aggregates data from multiple remote systems and performs automated analysis. This mediator handles the complex tasks of data collection, storage, and anomaly detection, allowing individual remote systems to remain relatively simple while achieving sophisticated centralized monitoring capabilities.
Solution Approach 2:
The system creates copies of data from remote systems and stores them in a centralized database. By working with data copies rather than directly analyzing remote systems, the patent enables comprehensive automated analysis without adding complexity to the original systems. The database server analyzes copied statistics and generates anomaly reports without requiring changes to the remote systems themselves.
2Loss of time
If automated detection systems are deployed, then response time to issues is improved, but resource consumption increases
Solution Approach 1:
The database server performs automated analysis of aggregated data on periodic intervals rather than continuously. This periodic operation allows the system to detect anomalies in a timely manner while conserving computational resources during intervals between analysis cycles. The system balances responsiveness with resource efficiency by scheduling automated detection tasks at appropriate intervals.
Data Source
AI summary
A method for detecting hardware and/or software anomalies in remote systems. The method may include aggregating, in a centralized electronic database, by an electronic database server, data received via a network from each of the remote systems, the data relating to operating statistics of one or more subcomponents of the remote systems over time. The method may also include utilizing an electronic database client communicatively coupled to the centralized database to automatically periodically access and analyze data stored in the centralized database to identify anomalies in hardware and/or software components of the remote systems. In one embodiment, the data relating to operating statistics of the subcomponents may include data from statistics counters corresponding to the subcomponents, each statistics counter, in one state, indicative of an identifiable error. In this regard, analyzing data stored in the centralized database may involve comparing data from the statistics counters to identify the anomalies.