Transaction Rule Organization for Efficient Pattern Matching
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Solution Overview
Problem
Existing methods for identifying transaction types in network services are inefficient, often requiring significant time and computing power, and fail to adapt to changes in transaction frequencies.
Innovation Solution
A network service monitoring system that applies a set of rules to communications between clients and servers to identify transaction patterns, with rules organized based on expected frequencies, allowing for efficient processing and updating based on actual frequency data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to identify transaction types, then transaction identification can be performed, but it requires significant time and computing power
Solution Approach 1:
The patent segments transaction identification into two phases: offline rule generation and online pattern matching. Offline, comprehensive rules are generated from historical data. Online, only relevant rules are applied using optimized data structures (tries, graphs), dramatically reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary rule generation and organization before actual transaction monitoring. By pre-processing historical transaction data to create organized rule sets with frequency information, the system avoids time-consuming analysis during real-time transaction identification.
2Measurement precision
If comprehensive rules are applied to identify all transaction types, then identification accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent applies local quality by organizing rules based on frequency and characteristics of specific transaction patterns. High-frequency patterns are prioritized and organized in optimized data structures, while low-frequency or rare patterns are handled differently, allowing the system to maintain accuracy for common transactions with minimal computational resources.
Solution Approach 2:
The patent changes the parameter of rule organization by incorporating frequency data and transaction characteristics into the rule structure. Rules are organized not just by pattern matching but by frequency-weighted categories, enabling the system to focus computational effort on the most significant transaction types.
3Adaptability or versatility
If static transaction patterns are monitored, then monitoring simplicity is maintained, but the system cannot adapt to changes in transaction frequencies
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors actual transaction frequencies and compares them against expected patterns. When deviations are detected, the system can update its rule organization and frequency expectations, enabling adaptation to changing transaction behaviors while maintaining a structured monitoring approach.
Data Source
AI summary
Efficiently identifying transactions processed by a software application, such as a server application is disclosed. In one embodiment, transactions are identified by applying a set of rules to communications between a client and server to determine whether certain patterns are in the communications. For example, the rules may look for some combination of parameters in the transactions. As a particular example, the rules may be used to look for parameters in HTTP requests. The rules are organized in a way that allows efficient processing. For example, the rules may be organized based on the frequency with which the parameters are expected to occur in the transactions and the frequency with which each transaction is expected to occur. The rules may be updated if the expected frequencies deviate from actual frequencies, such that the rules can be organized for more efficient processing.