Dynamic Metric Search Frequency for Asset Monitoring
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Solution Overview
Problem
Modern operational systems face challenges in processing and analyzing large volumes of machine-generated data from diverse sources, as existing tools often discard non-preprocessed data, limiting flexibility and insight into complex machine data sets.
Innovation Solution
An event-based data intake and query system, such as the SPLUNK® ENTERPRISE system, uses a late-binding schema to store and analyze minimally processed machine data, enabling flexible search and extraction of insights across disparate data sources, with features like flexible schema, extraction rules, and parallel processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional data processing tools are used to process machine-generated data, then data processing can be performed with simple preprocessing, but flexibility and insight into complex machine data sets are limited
Solution Approach 1:
The patent implements dynamic search frequencies for different metric types, allowing the system to adaptively adjust data collection intensity based on specific monitoring needs. Critical metrics are queried more frequently than non-critical ones, enabling flexible response to changing system conditions without uniform overhead
Solution Approach 2:
The system changes the parameter of search frequency dynamically based on metric type and system state. By adjusting query intervals and data collection parameters according to specific monitoring requirements, the system achieves flexibility in handling diverse machine data sets while optimizing resource utilization
2Reliability
If uniform high-frequency monitoring is applied to all metrics, then real-time visibility is improved, but system resources are wasted on non-critical data
Solution Approach 1:
The patent applies different monitoring qualities to different metric types. Critical infrastructure metrics receive high-frequency monitoring with detailed collection, while less important metrics use lower-frequency sampling. This localized quality adjustment ensures reliable monitoring of essential elements without wasting resources on comprehensive collection of all data
Solution Approach 2:
The system performs partial monitoring actions based on priority levels. For non-critical metrics, it collects only essential data at reduced frequency rather than complete high-frequency monitoring. This partial action approach maintains adequate monitoring accuracy for less important metrics while significantly reducing overall resource consumption
3Loss of information
If comprehensive data collection is performed for all assets, then complete insight is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and collects only the specific metric data needed for particular monitoring objectives rather than comprehensively collecting all possible asset data. By taking out only the relevant information required for each monitoring scenario, the system achieves adequate data completeness for decision-making while reducing processing time and computational overhead
Solution Approach 2:
The monitoring system segments data collection by metric type, asset criticality, and monitoring priority. Different segments of the asset portfolio are monitored with appropriate data collection intensities. This segmentation allows the system to maintain complete insight into critical assets while using efficient sampling for less important ones, optimizing the balance between data completeness and processing time
Data Source
AI summary
An example method comprises: causing display of a user interface comprising a plurality of dynamic elements, the user interface to facilitate configuring a search frequency for metrics associated with the plurality of dynamic elements, wherein each metric represents a respective point in time or a period of time and is derived from a metric-time search of machine data associated with a respective asset node; and for each dynamic element of the plurality of dynamic elements: receiving, via the user interface, a search frequency for a metric associated with the dynamic element; and determining a value of the metric by executing, according to the search frequency for the metric, a search query associated with the dynamic element.