Application Intelligence via Usage Learning in Event Driven Systems
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Solution Overview
Problem
In Service-Oriented Architecture (SOA) Governance, tracking and monitoring the lifecycle states and events of applications are difficult, making it challenging to gather data for decision-making, maintenance, and anomaly resolution, and existing event-driven auditing techniques fail to build intelligent systems by interpreting audit logs effectively.
Innovation Solution
An approach that records application evolution and usage to identify interaction patterns, generate inference documents capturing user intentions, and create custom application workflows based on these patterns, enabling the application to adapt and evolve dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If event-driven auditing techniques are used to track application lifecycle states and events, then data gathering capability is improved, but the ability to build intelligent systems by interpreting audit logs remains insufficient
Solution Approach 1:
The patent introduces an intermediary system that sits between the event-driven auditing mechanism and the application intelligence layer. This intermediary automatically interprets audit logs and transforms raw event data into meaningful application intelligence, bridging the gap between data collection and intelligent decision-making without requiring manual intervention.
Solution Approach 2:
The system enables applications to automatically generate their own intelligence by interpreting their own audit logs. The application uses the gathered event data to autonomously understand its own behavior patterns, workflow variations, and performance characteristics, eliminating the need for external analysis tools.
2Measurement precision
If detailed recording of application evolution and usage is implemented, then usage pattern analysis is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-defining workflow templates and interaction patterns before actual application execution. During runtime, the system only needs to match observed events against these pre-established patterns rather than analyzing raw data from scratch, significantly reducing processing complexity while maintaining high measurement precision.
Solution Approach 2:
The system transforms detailed usage data into simplified parameters by aggregating event sequences into meaningful workflow instances. Instead of processing every individual event, the system converts streams of events into parameterized workflow representations that capture essential usage patterns with reduced data volume and complexity.
3Adaptability or versatility
If custom application workflows are generated based on interaction patterns, then application adaptability is improved, but workflow management complexity increases
Solution Approach 1:
The patent implements dynamics by making workflows adaptive rather than static. Workflows are automatically generated and updated based on observed interaction patterns, allowing the system to dynamically adjust to changing usage requirements. The workflow management system evolves alongside application usage without requiring manual reconfiguration.
Solution Approach 2:
The system simplifies workflow management by copying and reusing proven workflow patterns across different application instances. Instead of managing unique workflows for each application, the system creates templates from observed patterns and automatically instantiates them, reducing management complexity through replication and standardization.
Data Source
AI summary
Certain example embodiments relate to application intelligence gathering systems and/or methods, e.g., in connection with Event Driven Applications and/or the like. More particularly, certain example embodiments relate to the effective recording of application evolution and usage information for usage learning and/or event auditing purposes. With respect to usage learning, certain example embodiments may help to capture data on the usage patterns and/or apply learning algorithms that work on the captured data to provide required intelligence to the application. With respect to event auditing, certain example embodiments may help to identify the “who”, “what”, “when”, “where”, “how”, and/or “why” of particular operations. Application intelligence optionally may be used in determining application “hotspots” or commonly used features that could help in areas such as application maintenance, performance tuning, and/or the like.