Model-Driven Event Detection System for Business Intelligence
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Solution Overview
Problem
Businesses face challenges in obtaining a complete view of their operating environment due to the vast amount of information available, leading to incomplete understanding of patterns and their implications, which results in inaccurate resource availability assessments and formulation of strategies, thereby increasing risk and limiting growth.
Innovation Solution
An event analysis system that customizes models for specific businesses to analyze information from various sources, detect relevant events, infer new events, and report them, using an information source model, entity relationship model, event type model, and implication rules to filter and process data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If an individual manually retrieves and reads information from multiple sources, then complete information coverage is achieved, but the time required becomes impractically long
Solution Approach 1:
The system enables automated self-service information processing where the event analysis system automatically retrieves, filters, detects events, infers implications, and generates reports without requiring manual human intervention for each information source, thus achieving complete information coverage while eliminating the time constraint
Solution Approach 2:
The patent replaces the mechanical manual process of reading and analyzing information with an automated computational system that uses event detection engines, implication engines, and model-driven analysis to process information sources, transforming the manual mechanical task into an automated intelligent system
2Reliability
If all available information is analyzed in detail, then comprehensive understanding is achieved, but the complexity of processing increases significantly
Solution Approach 1:
The system segments the complex information processing task into distinct modular components: information source model, entity relationship model, event type model, event detection engine, implication engine, and report generator. Each module handles a specific aspect of analysis, reducing overall system complexity while maintaining comprehensive analysis capability
Solution Approach 2:
The patent introduces model-driven intermediaries (event models, entity relationship models, implication rules) that act as mediators between raw information and business insights. These models structure and organize information in standardized formats, simplifying the processing complexity while ensuring reliable and complete analysis
3Measurement precision
If manual filtering and interpretation of information patterns is performed, then accurate event detection is achieved, but productivity decreases due to time consumption
Solution Approach 1:
The system replaces manual filtering and interpretation with automated event detection engines that use predefined event models and patterns to identify relevant events, and implication engines that automatically infer business impacts. This substitution maintains detection accuracy through structured model-driven analysis while dramatically increasing productivity by eliminating manual processing time
Solution Approach 2:
The patent implements preliminary action by pre-defining event models, entity relationship models, and implication rules before analysis begins. These pre-configured models enable the system to quickly detect and interpret events without requiring real-time manual analysis, thus maintaining accuracy through structured approaches while improving speed through prepared analytical frameworks
Data Source
AI summary
An event analysis system monitors information available from both publicly and privately distributed networks of information for events that are relevant to the user's particular business concerns. Those concerns are defined in a customized model of the user's organization and external business environment. The system receives the information, detects events in the information, interprets the events, and determines implications of these events. The detection and implication proceeds with regard to specific entities, relationships between entities, and definitions of the types of events which may occur in the environment in which the entities exist. Accordingly, the analysis system intelligently adapts its processing to recognize and report events which may be of interest for any particular entity.