Real-Time Business Event Processing System with Near Zero Latency
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Solution Overview
Problem
Current operational business intelligence systems face challenges in achieving real-time data processing and decision-making due to latency issues in data, reporting, and analysis, which hinder immediate action in response to business events.
Innovation Solution
The development of a system that models, monitors, aggregates, and correlates business events in real-time, using analytic models with fields, rules, timers, and actions to process events with near zero latency, enabling near real-time business activity monitoring and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional business intelligence systems are used to process business events, then historical information can be analyzed, but real-time processing capability is lost due to data latency, reporting latency, and analysis latency
Solution Approach 1:
The system performs preliminary actions by pre-compiling and indexing historical business event data in advance. This allows the system to quickly compare current events against pre-prepared patterns without performing full analysis in real-time, thereby reducing data processing latency while maintaining analytical depth.
Solution Approach 2:
The business intelligence system is segmented into independent modular components: data collection modules, pattern recognition modules, analysis modules, and reporting modules. Each module processes specific aspects of business events independently and in parallel, eliminating sequential processing bottlenecks and reducing overall data latency while maintaining comprehensive analysis capabilities.
2Measurement precision
If comprehensive business event analysis is performed, then accurate insights are obtained, but processing time increases due to the complexity of analyzing huge volumes of data
Solution Approach 1:
The system applies partial action by initially analyzing only the most critical or high-impact business events using simplified models, while more comprehensive analysis is applied selectively to events requiring deeper investigation. This reduces overall analysis latency while maintaining sufficient accuracy for time-sensitive decisions.
Solution Approach 2:
The system replaces traditional mechanical sequential analysis with intelligent automated analysis using AI algorithms and machine learning models. These intelligent systems can process and correlate huge volumes of business event data in parallel, maintaining high analysis accuracy while dramatically reducing the time required for comprehensive business intelligence.
3Productivity
If real-time data processing is implemented, then immediate business decisions are enabled, but system complexity increases to handle multiple data sources and simultaneous queries
Solution Approach 1:
The system implements universal multi-functional components that can handle multiple types of business events, data sources, and analysis requirements through a single unified platform. This universal architecture reduces overall system complexity compared to having separate specialized systems for each function, while enabling immediate business decisions across diverse operational areas.
Solution Approach 2:
The system introduces intermediary layers including event buffers, message queues, and abstraction layers that mediate between multiple data sources and the core processing engine. These intermediaries simplify the interface complexity by providing standardized protocols and buffering mechanisms, allowing real-time processing of simultaneous queries from multiple sources without overwhelming the core system.
Data Source
AI summary
Methods, systems, and computer program products for monitoring, aggregating, and correlating business events in real time and acting on the results with near zero latency, wherein each event is processed in the first order relative to the event density, are described herein. In an embodiment, the method operates by receiving historical values comprising keys and data fields at an analytic model. Rules associated with actions are applied to the historical values. Actions including updating data are executed pursuant to the rules, and then the method determines whether additional rules are to be applied; and performs actions associated with these additional rules until there are no remaining rules to apply. The method stores updated data in a database.