Expression-Tree Dashboard Monitoring for Cross-Platform Data Flows
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Solution Overview
Problem
Existing computer system monitoring tools lack the ability to monitor and report data flows across complex computing platforms with disparate components, failing to provide real-time operation intelligence and specific process monitoring.
Innovation Solution
A dashboard monitoring system that aggregates and processes metrics from disparate ecosystem components, dynamically constructing expression trees to derive functions for real-time view modeling and reporting, utilizing a query processor to navigate predicates and update views in response to user requests, and employing dashboard notifiers for asynchronous data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing monitoring tools are used, then simple system monitoring is possible, but real-time monitoring of data flows across complex computing platforms with disparate components cannot be achieved
Solution Approach 1:
The patent introduces a monitoring system that acts as an intermediary layer between disparate ecosystem components and users. This system collects metrics from multiple ecosystem components, processes them through a query processor, and delivers results via dashboard notifiers. The intermediary architecture enables real-time monitoring of data flows across complex platforms without requiring users to directly manage the complexity of individual components.
Solution Approach 2:
The monitoring system is divided into distinct segments: dashboard notifiers embedded in ecosystem components, a central monitoring system with query processor, and user interfaces. This segmentation allows each component to be developed, maintained, and scaled independently while working together to provide comprehensive real-time monitoring across the entire ecosystem.
2Loss of information
If comprehensive monitoring of all ecosystem components is implemented, then complete operation intelligence is achieved, but processing time and system resource consumption increase
Solution Approach 1:
The system implements selective monitoring by allowing users to specify which ecosystem components and metrics are relevant to their needs. The query processor can filter and aggregate only the necessary data rather than processing all available metrics from all components. This partial action approach maintains comprehensive monitoring capability while reducing processing time and resource consumption by focusing only on required information.
Solution Approach 2:
The monitoring system performs preliminary actions by continuously collecting and buffering metrics from ecosystem components in advance. The query processor can then quickly retrieve and process pre-collected data rather than querying components in real-time for each request, significantly reducing response time for monitoring queries.
3Measurement precision
If real-time data aggregation from multiple sources is performed, then accurate monitoring metrics are obtained, but data processing complexity and computational overhead increase
Solution Approach 1:
The patent merges data processing operations into a unified query processor that handles aggregation, filtering, and metric calculation in a single coordinated system. Rather than having separate processing pipelines for each ecosystem component, the query processor consolidates all data flow management and metric computation in one place, reducing overall processing complexity while maintaining measurement precision.
Solution Approach 2:
The query processor serves multiple functions: collecting metrics from diverse ecosystem components, validating data formats, aggregating data across time windows, filtering based on user criteria, and preparing results for delivery. This multi-functional universal processor reduces data processing complexity by eliminating the need for separate specialized processing systems for each function.
Data Source
AI summary
A new ecosystem monitoring solution provides novel features including a dashboard service, a dashboard notifier, and a near real time query processor. The dashboard service can continuously aggregate, through the dashboard notifier and optionally dashboard agents, metadata from disparate ecosystem components of a complex computing platform or ecosystem. The metadata include metrics of crawling, data ingestion, and content enrichment activities and health information of the disparate ecosystem components. The metrics are processed with respect to a time window utilizing an expression tree dynamically constructed by the query processor. The query processor can navigate the expression tree to form collection models. Each collection has aggregation functions for aggregating a set of metrics specified in a view model. Responsive to a view request, the view model can be dynamically updated utilizing the collection model and communicated to a user device for rendition and presentation of a view through a dashboard user interface.