Dictionary Creation Apparatus for Log Phrase Extraction
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Solution Overview
Problem
Creating a dictionary for a new apparatus can be time-consuming and labor-intensive, as the phrases characterizing its log may not be known until support tasks are performed, and existing dictionary customization methods are not efficient for new apparatuses.
Innovation Solution
A dictionary creation apparatus that extracts and processes character strings from logs of existing and new apparatuses to determine matching degrees, allowing for the registration of characteristic phrases into a new dictionary, thereby facilitating efficient dictionary creation for new apparatuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a dictionary is created manually for a new apparatus by extracting phrases from logs during support tasks, then the dictionary accurately reflects the specific log characteristics of that apparatus, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary extraction of characteristic phrases from logs of existing apparatuses before the new apparatus is fully deployed. By proactively creating candidate dictionaries using phrase extraction algorithms and similarity comparison, the system prepares dictionary candidates in advance, so that when the new apparatus needs support, the dictionary is already available or nearly ready, significantly reducing the time required for dictionary creation while maintaining accuracy through subsequent verification steps
Solution Approach 2:
The system creates dictionary candidates by copying and adapting phrases from existing apparatus dictionaries. It extracts characteristic phrases from logs of similar apparatuses, uses string similarity algorithms to identify matching patterns, and generates candidate dictionaries that replicate the structure and content of proven dictionaries. This copying approach allows rapid dictionary creation for new apparatuses based on existing templates, reducing manual work while preserving accuracy through similarity-based selection
2Adaptability or versatility
If characteristic phrases are extracted from logs of a new apparatus, then the dictionary is specific to that apparatus, but the phrases may not be known until support tasks are performed
Solution Approach 1:
The system extracts characteristic phrases from logs of existing apparatuses in advance, before the new apparatus generates its own logs. By performing phrase extraction and dictionary candidate generation proactively during the apparatus setup phase or using data from similar apparatuses, the system makes dictionary candidates available before support tasks begin, eliminating the waiting period while ensuring apparatus-specific accuracy through similarity-based matching
Solution Approach 2:
The system creates a multi-functional phrase extraction mechanism that can operate on logs from both existing apparatuses (for candidate generation) and new apparatuses (for verification). The same extraction algorithms and similarity comparison logic serve multiple purposes: generating candidates from existing data, validating candidates against new data, and adapting to different apparatus types. This universal approach enables the system to provide apparatus-specific dictionaries rapidly across different scenarios
3Productivity
If existing dictionary patterns are leveraged for new apparatuses, then dictionary creation is faster, but the new apparatus may have similar composition to existing ones
Solution Approach 1:
The system implements a feedback mechanism where candidate dictionaries generated from existing apparatus patterns are validated against logs of the new apparatus. The similarity comparison algorithm provides feedback on how well the candidate phrases match the actual log characteristics of the new apparatus. If the matching degree falls below a threshold, the system adjusts the candidate dictionary by incorporating phrases specifically from the new apparatus logs, ensuring that productivity gains from pattern leveraging do not compromise inference accuracy
Solution Approach 2:
The system creates a dynamic dictionary generation process that adapts based on the degree of similarity between existing and new apparatuses. When apparatuses are highly similar, the system heavily leverages existing patterns for rapid dictionary creation. When differences are detected, the system dynamically adjusts by incorporating more apparatus-specific phrases from logs. This dynamic approach optimizes the balance between creation efficiency and accuracy based on real-time similarity assessments
Data Source
AI summary
Embodiments generally relate to a solution to easily create a dictionary in which a character string to be extracted from a log of an apparatus is registered. In an example, a method includes extracting a first plurality of character strings from a first dictionary to determine types of the first plurality of extracted character strings, the first plurality of character strings having a common part at a part thereof, the a first dictionary including character strings extracted from a log of a first apparatus; extracting a second plurality of character strings having the common part from the log of the first apparatus to determine types of the second plurality of character strings; executing a first determination processing that includes determining whether the types of the first plurality of character strings are limited with respect to the types of the second plurality of character strings.