Unified Context Platform for Multi-Source Data Consolidation
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Solution Overview
Problem
Current monitoring tools often require end users to navigate multiple systems to understand how different technology processes, such as server loads and user interactions, impact overall business operations, making it difficult to generate comprehensive context for resource allocation and decision-making.
Innovation Solution
A platform that collects and processes data from various sources, allowing users to obtain micro-application actor information, apply data set and functional expressions, and perform processing actions based on generated outputs to provide contextual insights, reducing the need to switch between systems and enabling quicker decision-making through visual presentations and alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If monitoring tools are hosted in separate systems with different formats, then each system can be optimized for its specific task, but end users must navigate multiple tools to generate comprehensive context
Solution Approach 1:
The patent consolidates multiple separate monitoring tools into a unified platform that collects and processes data from various sources (servers, databases, cloud services) in a single system. This merging eliminates the need for users to navigate between multiple tools while maintaining the optimized functionality of each original tool through modular data processing components.
Solution Approach 2:
The patent introduces a central processing platform that acts as an intermediary between disparate data sources and users. This intermediary layer standardizes data formats, applies transformations, and presents unified context, thereby resolving the incompatibility issue between separate systems without requiring changes to the original optimized tools.
2Ease of operation
If monitoring tools are integrated into a single system, then end users can easily navigate and access context, but the system complexity increases
Solution Approach 1:
The patent segments the integrated monitoring system into independent modular components, each responsible for specific functions (data collection, transformation, processing, visualization). This segmentation allows the system to maintain simplicity through clear separation of concerns while achieving integration benefits, as each module can be developed and maintained independently.
Solution Approach 2:
The patent creates a universal processing platform that handles multiple types of data from various sources through standardized interfaces. This multi-functionality approach allows a single system to perform diverse monitoring tasks without requiring complex specialized components for each task, thereby reducing overall system complexity.
3Loss of information
If comprehensive data collection from multiple sources is implemented, then contextual insights improve, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data transformations, filtering, and aggregations at the source level before data reaches the central processing platform. By preparing data in advance through ETL (extract, transform, load) processes, the system reduces the computational burden during analysis while maintaining comprehensive contextual insights.
Solution Approach 2:
The patent implements selective data collection and processing, focusing computational resources on the most critical data sources and time windows. Rather than processing all available data uniformly, the system applies partial processing strategies that prioritize high-value information, thereby reducing overall processing time while preserving essential contextual insights.
Data Source
AI summary
Disclosed are methods, systems, and non-transitory computer-readable medium for context development. For instance, a first method may include obtaining first micro-application actor information associated with a first micro-application actor. The first micro-application actor information may include information for workflow rules, and the workflow rules may include data set rules, extract, transform, load (ETL) rules, and functional expressions. The first method may further include obtaining data from data sources based on the data set rules; applying the ETL rules to the obtained data to generate processed data; applying the functional expressions to the processed data to obtain an output; and performing at least one processing action based on the output. A second method may include generating a system component corresponding to a blueprint based on a user request; associating the system component with a domain of a user account; and performing processes associated the system component.