Logical Inference Query Generation for Complex Data Search
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Solution Overview
Problem
Current search systems struggle to exhaustively extract data related to a series of occurrences with potential relationships from a dataset, as it is difficult to describe these relationships in queries, limiting their ability to retrieve relevant information, especially in complex scenarios like cyber attack analysis.
Innovation Solution
An information search apparatus and method that employs a logical inference unit to generate data of series occurrences using rules, a query generation unit to determine conditions for extracting relevant data, and a data search unit to execute searches using these queries, facilitating the extraction of data with potential relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a query is constructed to describe relationships among data, then the ability to extract data related to series of occurrences improves, but the difficulty of constructing the query increases
Solution Approach 1:
The system performs preliminary logical inference to generate hypothetical series of occurrences and automatically construct queries based on these hypotheses before executing the search. This preliminary action transforms the complex task of manual query construction into an automated process that achieves high search accuracy without requiring users to manually describe complex relationships
Solution Approach 2:
The system introduces an intermediary logical inference engine that acts as a mediator between the search requirements and the data retrieval process. This intermediary automatically generates queries based on inferred relationships, eliminating the need for users to directly construct complex relationship descriptions while maintaining high search accuracy
2Measurement precision
If only user-entered queries are used for search, then the simplicity of the search system is maintained, but the search accuracy is limited
Solution Approach 1:
The search system is enhanced with multi-functionality by integrating logical inference capabilities alongside traditional query processing. The system can now both execute user-entered queries and automatically generate queries based on logical inference from observed occurrences, achieving higher search accuracy while maintaining the original simple query interface
Solution Approach 2:
The system performs self-service by automatically generating search queries based on logical inference from the data itself. Rather than relying solely on user input, the system autonomously analyzes observed occurrences, infers relationships, and constructs appropriate queries, thereby improving search accuracy without significantly increasing user-facing complexity
3Productivity
If manual query description of relationships is required, then the interpretability of the search is maintained, but the exhaustiveness of data extraction decreases
Solution Approach 1:
The system performs preliminary logical inference to generate comprehensive hypotheses about series of occurrences before execution. This preliminary action enables the system to automatically construct queries that cover all potential relationships in the data, achieving exhaustive data extraction without requiring manual description of each relationship
Solution Approach 2:
The logical inference engine serves as an intermediary that automatically translates observed occurrences into comprehensive queries covering all potential relationships. This intermediary handles the complex task of describing relationships, achieving exhaustive data extraction while relieving users from the burden of manual relationship description
Data Source
AI summary
The information search apparatus 100 includes: a logical inference unit 10 that executes a logical inference by applying a rule indicating a relationship between occurrences to a set of data indicating observed occurrences, and generates data of a series of occurrences through the logical inference; a query generation unit 20 that determines a condition for extracting data related to the series of occurrences from the set of data indicating observed occurrences based on the data of the series of occurrences and the rule indicating a relationship between occurrences, and generates a query using the condition determined; and a data search unit 30 that executes search processing on the set of data indicating observed occurrences using the query generated.