Custom Data Aggregation Service for Enterprise Integration
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Solution Overview
Problem
Enterprises face challenges in effectively combining, presenting, and timing data for decision-making due to frequent changes in data sources, software services, and inadequate customization of data interfaces, leading to inadequate information derivation and reporting.
Innovation Solution
A method and system for custom data aggregation and integration processing, where data from multiple terminals is collected in real-time, selectively aggregated, translated into a target format, and provided to resources based on evaluated conditions, allowing for dynamic customization and timely reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is collected from multiple sources in real-time, then the quantity and timeliness of data is improved, but the complexity of data aggregation and integration increases
Solution Approach 1:
The patent introduces an intermediary data integration service that sits between multiple data sources and consuming applications. This service receives data from various sources, performs aggregation and translation, then delivers integrated data to applications. The intermediary handles the complexity of data integration internally while presenting a simplified interface to both data producers and consumers, thereby resolving the contradiction between collecting data from multiple sources and managing the resulting integration complexity.
2Adaptability or versatility
If data interfaces are customized to specific enterprise needs, then the adaptability and relevance of data services is improved, but the ease of manufacture and deployment deteriorates
Solution Approach 1:
The patent implements dynamic data interfaces that can be configured and adapted at runtime based on enterprise needs. Rather than requiring custom development for each enterprise, the system allows dynamic configuration of data sources, transformation rules, and consumption patterns. This dynamic approach enables high adaptability while maintaining ease of deployment through configuration rather than custom coding, resolving the contradiction between customization and ease of manufacture.
Solution Approach 2:
The patent creates a universal data integration service that can serve multiple enterprises and use cases through a single platform. The service handles diverse data sources and consumption patterns using a unified architecture with configurable parameters. This universal design provides adaptability to specific enterprise needs through configuration while avoiding the need for separate custom-developed interfaces for each enterprise, thereby resolving the contradiction between customization and ease of manufacture.
3Loss of time
If data aggregation is performed continuously, then the timeliness of information reporting is improved, but the loss of energy and computational resources increases
Solution Approach 1:
The patent implements periodic data aggregation and reporting mechanisms that balance timeliness with resource efficiency. Instead of continuous processing, the system aggregates data at optimized intervals based on data type, source, and consumption requirements. This periodic approach maintains acceptable information freshness while significantly reducing computational overhead and energy consumption compared to continuous aggregation, resolving the contradiction between reporting timeliness and resource consumption.
4Loss of information
If data translation to target formats is performed for all data, then the completeness of information delivery is improved, but the productivity of data processing deteriorates
Solution Approach 1:
The patent implements selective data translation that applies format conversion only where necessary. The system analyzes data sources, target requirements, and consumption patterns to determine which data elements require translation to target formats. This local quality approach ensures format compatibility for data that needs it while leaving other data in its native format, thereby maintaining information completeness while preserving data processing throughput and avoiding unnecessary translation overhead.
Data Source
AI summary
Agents are configured to collect data from terminals and send the data in real time to a data integrator. The data integrator identifies the data types and processes custom aggregation on select ones of data types from the data. The data and the output of any aggregations are translated to a select output format and sent to one or more select resources for further processing. The data integrator determines when to send the data and the output from any aggregation in the select output format to the one more select resources based on a defined condition. In an embodiment, the data integrator sends the data and output from any aggregation in real time to at least one of the one or more select resources.