Behavior Analysis Engine for Log Diagnosis
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Solution Overview
Problem
Modern computer systems face challenges in diagnosing system problems due to the complexity of system-generated logs and the lack of tools for intelligently representing system knowledge, leading to increased time spent by operators in determining the root cause of issues.
Innovation Solution
A Behavior Analysis Engine (BAE) system that combines machine learning with expert knowledge to build industry-specific knowledge bases, converting unstructured log data into a well-defined structure, and providing a framework for log exploration and querying using semantic language, leveraging big data platforms to efficiently manage system anomaly behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operators manually analyze system-generated logs to diagnose problems, then diagnostic accuracy can be achieved, but the time required increases significantly
Solution Approach 1:
The patent introduces an automated log analysis system that acts as an intermediary between the raw logs and the operator. This system uses machine learning models and knowledge bases to automatically interpret logs, identify anomalies, and generate diagnostic reports, thereby reducing the time operators need to spend on manual analysis while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-service diagnostics by automatically analyzing logs and generating diagnostic conclusions without requiring extensive manual operator intervention. The automated analysis engine processes logs, identifies patterns, and provides diagnostic recommendations independently, freeing operators from time-consuming manual tasks.
2Adaptability or versatility
If comprehensive log analysis is performed to cover more system behaviors, then knowledge base coverage improves, but system complexity increases
Solution Approach 1:
The patent segments the log analysis system into modular components including log ingestion modules, parsing modules, machine learning analysis modules, and knowledge base modules. Each module handles specific aspects of log analysis, making the overall system more manageable and easier to maintain while comprehensively covering diverse system behaviors through specialized sub-components.
Solution Approach 2:
The system employs universal log parsing templates and multi-purpose machine learning models that can handle various types of system logs across different applications. This multi-functionality allows the system to cover comprehensive system behaviors without proportionally increasing complexity, as the same core components serve multiple analysis purposes.
3Productivity
If automated query optimization is implemented to improve query processing, then query execution speed increases, but the initial processing overhead increases
Solution Approach 1:
The system performs preliminary actions by pre-compiling and optimizing query plans during idle periods or in advance of actual query execution. Query templates are pre-processed and stored in optimized forms, so when actual queries are executed, the optimization overhead has already been incurred during the preliminary phase, resulting in faster execution speeds with minimal additional overhead.
Solution Approach 2:
The query optimization system dynamically adjusts its approach based on query characteristics, data distribution, and system state. Rather than applying fixed optimization rules, the system adapts its optimization strategies in real-time, selecting the most appropriate optimization techniques for each specific query scenario, thereby improving execution speed while minimizing unnecessary overhead.
Data Source
AI summary
Systems and methods for optimizing query execution to improve query processing by a computer are provided. A query is analyzed and translated into a logical plan. A runtime query optimizer is applied to the logical plan to identify a physical plan including operators for execution. The logical plan is translated into the physical plan. Execution of the query is scheduled according to the physical plan.