Distributed Data Analysis Broker Segmentation
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Solution Overview
Problem
Traditional data analysis solutions face challenges in managing large volumes of data, requiring resource-intensive systems and complex configurations, with limitations in scalability, compatibility, and handling unforeseen failures or data loss, making it difficult to efficiently analyze and utilize data for problem identification and resolution.
Innovation Solution
A distributed data analysis system using a data broker that distributes data to consumers, allowing for dynamic subscription and processing of data, enabling scalable analysis and extensibility, with components that can use previously analyzed results to reduce data volume and facilitate flexible data processing, including error detection and recovery mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional centralized data analysis systems are used to manage large volumes of data, then data can be stored and accessed, but computing resources and time required for sorting and searching increase significantly
Solution Approach 1:
The patent divides the centralized data analysis system into multiple distributed analysis engines that operate independently across different nodes. Each engine processes subsets of data locally, eliminating the need to sort and search entire datasets centrally. This segmentation reduces both computing resource requirements and time for data processing operations.
Solution Approach 2:
The system transitions from a single-dimensional centralized processing model to a multi-dimensional distributed architecture where data analysis occurs across spatial (multiple nodes) and functional (different analysis engines) dimensions simultaneously, enabling parallel processing that reduces time complexity.
2Loss of information
If statistical analysis is performed on large databases to extract useful information, then meaningful insights can be obtained, but the analysis requires advanced training to understand and translate results
Solution Approach 1:
The patent introduces intermediary components including standardized data interfaces and automated result translation mechanisms that bridge the gap between complex statistical analysis and business applications. These intermediaries automatically interpret and translate analysis results into actionable business intelligence, eliminating the need for manual translation by experts.
Solution Approach 2:
The system implements universal data interfaces and standardized analysis frameworks that can handle multiple types of data and analysis tasks through common mechanisms. This universality simplifies the complexity by providing consistent methods for information extraction across different domains without requiring domain-specific expertise for each case.
3Productivity
If distributed data analysis is implemented to improve scalability, then system can handle larger data volumes, but system complexity and configuration difficulty increase
Solution Approach 1:
The patent implements self-service mechanisms where the distributed analysis system automatically configures itself, discovers data sources, and optimizes resource allocation without requiring complex manual configuration. The system performs self-diagnosis and adaptive configuration, reducing the complexity burden on users while maintaining scalability.
4Reliability
If comprehensive data collection is performed to analyze software and hardware problems, then complete problem analysis can be achieved, but the analysis becomes overwhelming and solutions become extremely complex
Solution Approach 1:
The patent extracts and separates specific problem analysis functions from the overall data collection system. Instead of analyzing all collected data comprehensively, the system identifies and extracts only the relevant data subsets needed for specific problem types, reducing analysis complexity while maintaining reliability for targeted problem detection and resolution.
Data Source
AI summary
Distributed data analysis systems and methods are provided. A data broker distributes received data to consumers, such as information and repository consumers, which can be subscribed to the data. A subsystem with a processor for data processing can provide data to the data broker. A first information consumer may include a receiving module for receiving the data from the data broker, an analysis module for analyzing the received data to obtain a result, and a publication module for sending the result to the data broker when a second information consumer is subscribed to the result.