Log Data Feature Extraction for Maintenance Event Retrieval
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Solution Overview
Problem
Existing information providing systems for maintenance work lack the ability to effectively retrieve past events with temporal information, which is crucial for accurate reference and decision-making during maintenance operations.
Innovation Solution
An information providing system that acquires log data from information equipment systems, extracts features from this data, and retrieves relevant information from historical records based on these features, allowing for the inclusion of temporal information in event retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If past events are retrieved without temporal information, then the retrieval process is simpler and faster, but the accuracy and relevance of retrieved events deteriorates
Solution Approach 1:
The system performs preliminary extraction of temporal information from log data and stores it in a structured format before the actual retrieval operation. This pre-processing of temporal features allows the retrieval system to quickly match and filter events based on time conditions without adding significant complexity to the core retrieval mechanism.
Solution Approach 2:
The system changes the parameter representation by extracting temporal information as distinct features from the log data and storing them in a normalized format. This parameter transformation enables efficient temporal filtering and matching during retrieval operations, improving accuracy while maintaining system simplicity through standardized data representation.
2Reliability
If temporal information is included in event retrieval, then the relevance of retrieved events improves, but the retrieval time and processing complexity increases
Solution Approach 1:
The system extracts and pre-processes temporal information from log data in advance, storing it in a structured format that enables rapid querying. This preliminary action separates the time-consuming extraction process from the retrieval operation, allowing fast searches based on temporal conditions without reprocessing the entire log data.
Solution Approach 2:
The system extracts temporal information as a separate, independent feature from the main log data. By taking out time-related attributes and storing them in a dedicated structure, the system enables efficient temporal filtering during retrieval without processing the entire log content, thus reducing retrieval time while maintaining high relevance.
3Measurement precision
If feature extraction from log data is performed, then the accuracy of event matching improves, but the processing complexity and computational resources increase
Solution Approach 1:
The system segments the log data into distinct features, separating temporal information from other data elements. This segmentation allows the system to process and extract specific characteristics (time, event type, severity) independently, improving matching accuracy while reducing overall processing complexity through modular feature extraction.
Solution Approach 2:
The system extracts specific features from the log data, such as temporal information and event characteristics, and stores them in a normalized format. This extraction process focuses computational resources only on the most relevant features for matching, improving accuracy while reducing processing complexity by avoiding unnecessary analysis of irrelevant data.
Data Source
AI summary
An information providing system acquires log data generated by an information equipment system, extracts a feature of the log data, acquires, from history information related to the information equipment system, information based on the feature, and outputs the acquired information.