J2EE Data Provider Semantic Log Transformation
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Solution Overview
Problem
Enterprise threat detection systems face challenges in processing and comparing log data from heterogeneous systems due to the lack of semantic context, requiring significant computational resources and complicating data comparisons.
Innovation Solution
Implementing a JAVA 2 Platform, Enterprise Edition (J2EE) Data Provider to establish semantic connectivity between JAVA application servers and Enterprise Threat Detection systems, ensuring log data is transformed into a common format for efficient processing and comparison, while avoiding data duplication through timestamp-based extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If log data is collected from heterogeneous systems without semantic context, then data collection is simplified, but data comparison becomes complicated and requires large computational resources
Solution Approach 1:
The patent applies preliminary action by transforming log data into a common semantic format at the source (log-producing systems) before transmission to the ETD system. The J2EE Data Provider and log extractors perform semantic enrichment and format standardization in advance, so that when data reaches the ETD system, it is already structured for efficient comparison and analysis, eliminating the need for complex post-collection transformation.
2Productivity
If log data is transformed into a common semantic format at the source, then data processing efficiency improves, but implementation complexity increases
Solution Approach 1:
The patent implements universality through the J2EE Data Provider, which serves multiple functions: it acts as a standard interface for log extraction from Java application servers, performs semantic transformation to a common format, and integrates with the ETD system. This multi-functional component eliminates the need for separate transformation tools for each log source, standardizing the process across heterogeneous systems while maintaining manageable implementation complexity.
3Productivity
If timestamp-based extraction is used to avoid data duplication, then data transmission efficiency improves, but requires precise timestamp management
Solution Approach 1:
The patent applies feedback through the recovery point mechanism with recovery timestamps. The ETD system monitors data reception and uses timestamps to determine whether log data has already been processed. When the log processing job encounters a recovery point, it checks the timestamp to verify if data was previously transmitted, preventing duplication while ensuring complete data coverage. This feedback loop maintains precise timestamp management.
Data Source
AI summary
A log processing job executing on a log producing computing system is initiated for processing log data associated with the log producing computing system. Log entries are determined to be available for processing. At least one instance of a Log Extractor Factory, Reader, and Transformation component are instantiated for reading and transforming the log data. Read log data is transformed into a common semantic format as transformed log data and transmitted in real-time to a Streaming Component for storage in an Enterprise Threat Detection (ETD) System. A recovery point is stored with a recovery timestamp indicating a next log entry in the log data to process.