Coincidence Identification Tool for Dynamic Location and Time Queries
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Solution Overview
Problem
Existing systems for coincidence identification in databases lack the ability to dynamically adjust parameters for geographical locations and time frames, limiting the effectiveness of pattern recognition in large datasets.
Innovation Solution
A computer-implemented method that allows users to select parameters for geographical locations and time frames related to events, enabling the system to query data sources, group matching records, and display visual representations of coincidence entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed parameters are used for geographical locations and time frames in database querying, then the system structure remains simple, but the adaptability to different investigation needs deteriorates
Solution Approach 1:
The system implements dynamic parameters for geographical locations and time frames that can be adjusted by users during investigation. The query interface allows investigators to modify location boundaries and time period ranges, enabling the system to adapt to different investigation scenarios without requiring structural changes to the database architecture.
Solution Approach 2:
The system enables parameter changes in the query conditions, specifically allowing users to modify geographical location parameters (such as radius, coordinates) and temporal parameters (such as start time, end time). These parameter adjustments provide versatility in investigation while maintaining the same underlying system structure.
2Quantity of substance
If multiple data sources are queried simultaneously, then the quantity of occurrence records increases, but the difficulty of detecting and measuring patterns increases
Solution Approach 1:
The system segments the analysis process by first querying multiple data sources separately, then grouping occurrence records based on matching field values. This segmentation approach allows the system to handle large quantities of records from multiple sources while maintaining manageable processing steps and reducing the overall difficulty of pattern detection.
Solution Approach 2:
The system introduces an intermediary grouping mechanism that processes occurrence records from multiple data sources. By grouping records with matching field values before final analysis, this intermediary step simplifies the detection of coincidences and patterns across diverse data sources.
3Measurement precision
If comprehensive query parameters are used to ensure thorough investigation, then the measurement precision of coincidence identification improves, but the loss of time for processing increases
Solution Approach 1:
The system performs preliminary actions by pre-grouping occurrence records based on their field values before conducting the final coincidence analysis. This preliminary organization of data reduces the computational burden during the actual query execution, allowing comprehensive parameters to be used without excessive processing time delays.
Data Source
AI summary
User input is used to define different geographical locations and/or different time frames related to a plurality of events, At least one data source is queried to receive occurrence records corresponding to at least some of the plurality of events according to the different geographical locations and/or different time frames. The records having at least one field whose value matches are grouped to form at least one coincidence entity. A visual representation of the at least one coincidence entity is presented to the user.