Pseudo Parse Trees for Mixed Log Records
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Solution Overview
Problem
Existing question answering systems face difficulties in processing mixed records containing both structured and unstructured data, as they require specific knowledge of the record format, limiting their ability to generalize without manual intervention.
Innovation Solution
A method is introduced to create a pseudo-parse tree by linking structured and unstructured data using artificial syntactic dependencies, allowing the system to represent both data types as a single parse tree without needing prior knowledge of the record format, enabling the system to handle multiple types of logs or mixed records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If specific database queries with knowledge of log format are used to search for answers, then accurate answers can be obtained, but the system requires specific knowledge of the record format and cannot generalize to different log formats
Solution Approach 1:
The patent creates a universal pseudo-parse tree structure that can represent multiple log formats (security logs, ship logs, application logs) through a common schema. This universal structure allows the QA system to handle different log formats without requiring format-specific query knowledge, as all logs are transformed into the same pseudo-parse tree representation with standardized node types (ROOT, TIMESTAMP, LOCATION, EVENT, etc.).
Solution Approach 2:
The patent introduces an intermediary processing layer that converts diverse log formats into a standardized pseudo-parse tree structure before feeding them to the QA system. This intermediary transformation step acts as a mediator that eliminates the need for the QA system to understand specific log formats, as it only needs to query the standardized pseudo-parse tree structure.
2Productivity
If manual searching is used to find answers in logs, then accurate information can be retrieved, but the process is time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary action by automatically constructing pseudo-parse trees from log records before they are queried. This preprocessing step organizes the log data into a structured format with clear hierarchical relationships, so when a QA query arrives, the system can quickly search the pre-organized pseudo-parse tree instead of manually scanning unstructured log entries, significantly reducing retrieval time.
3Reliability
If structured and unstructured data are kept separate in the database, then data integrity is maintained, but it is difficult to construct connections between them for QA systems
Solution Approach 1:
The patent merges structured and unstructured data into a unified pseudo-parse tree structure. In this structure, structured data (timestamps, locations, event types) become specific node types within the tree, while unstructured data (event descriptions, messages) become text content associated with those nodes. This merging maintains data integrity through the structured tree hierarchy while enabling easy connection between structured and unstructured data through the tree's inherent relationships.
Data Source
AI summary
A method, apparatus and computer program product for creating parse trees from mixed records is described. A mixed record is received including a first entry containing a first natural language phrase and a second entry containing a first structured data entry. Using the first natural language phrase, a first parse tree structure is created joining words in the first natural language phrase with natural links indicating their semantic relationship within the first natural language phrase. A first synthetic node is created which represents the first structured data entry. The first synthetic is joined to the first parse tree structure using a synthetic link to a node in the parse tree to produce a pseudo parse tree. The pseudo parse tree is sent to a question answer system for answering user queries by reference to the pseudo parse tree.