Cognitive Data Aggregation Platform with Automated Rule Generation
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Solution Overview
Problem
The exponential growth of enterprise data from various sources, including unstructured and structured content, poses challenges in data retrieval, extraction, aggregation, and analysis, necessitating a system that optimizes data aggregation and analytics across multiple data sources for real-time, actionable insights.
Innovation Solution
A cognitive computing platform that automatically generates data extraction and adaptation rules using AI and advanced analytics, enabling real-time data monitoring, extraction, and analysis across multiple data sources, adapting to different data formats and presentation styles through a feedback loop, and generating optimization rules for actionable metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is extracted from multiple data sources using predefined rules, then data extraction can be performed systematically, but the system cannot handle unstructured or varying data formats effectively
Solution Approach 1:
The system employs automatic rule generation that enables the system to self-adapt to new data formats and sources without manual intervention. The automatic rule generation mechanism analyzes data patterns and creates extraction rules autonomously, allowing the system to serve itself in adapting to varying data structures while maintaining systematic extraction capabilities
Solution Approach 2:
The system incorporates feedback loops where extraction results are continuously analyzed and used to refine and update extraction rules. This feedback mechanism allows the system to learn from successful extractions and adapt to new data formats automatically, resolving the contradiction between adaptability and rule management complexity
2Reliability
If comprehensive data aggregation is performed across multiple sources, then complete insights can be obtained, but the processing time and system complexity increase significantly
Solution Approach 1:
The system performs preliminary data validation and filtering at the source level before aggregation, preparing data in advance for efficient processing. By validating data formats and structures upfront, the system reduces the computational burden during aggregation, maintaining complete insights while reducing processing time
Solution Approach 2:
The data aggregation process is divided into separate modular stages including data collection, validation, transformation, and analysis. This segmentation allows parallel processing of different data sources and reduces the overall processing time while maintaining comprehensive data aggregation through coordinated stage execution
3Measurement precision
If manual rule creation is used for data extraction, then extraction accuracy can be controlled, but the system cannot scale to handle exponential data growth
Solution Approach 1:
The automatic rule generation system enables self-service by autonomously creating and refining extraction rules based on data patterns, eliminating the need for continuous manual rule creation. This self-service capability maintains extraction accuracy through algorithmic precision while scaling to handle exponential data growth without proportional increases in manual effort
Solution Approach 2:
The system dynamically adjusts extraction parameters and rule configurations based on data characteristics and performance metrics. By automatically optimizing parameters such as extraction thresholds and transformation rules, the system maintains high extraction accuracy while improving processing throughput through efficient parameter tuning
Data Source
AI summary
A system and method for optimizing aggregation and analysis of data across multiple data sources of multiple enterprises is provided. Data extraction rules are generated by invoking an automatic rule generation rule. Automatic rule generation rule is invoked if a predefined set of rules is not applicable for extracting data. Further, data adaptation rules is generated if data extracted by applying data extraction rules and a predefined set of rules does not correspond to a predetermined output. The data adaptation rules is encapsulated in a feedback loop for transmitting to the data acquisition unit. Data optimization rules s generated based on data extraction rules and data adaptation rules. One or more metrics is generated based on data optimization rules. The metrics specify characteristics relevant to one or more enterprises.