Catalog-Based Data Inheritance Filtering for Relative Identification
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Solution Overview
Problem
Identifying relatives in large-scale genealogy databases is challenging due to the sheer amount of data and the computational infeasibility of comparing datasets without a concrete strategy, as datasets may not be connected without proper determination of their relationship, and real-world events leading to data inheritance are often undocumented.
Innovation Solution
A computer-implemented method to determine normalized data inheritance by generating a catalog of overrepresented data strings, comparing matched data strings, and excluding subsets to identify related data instances based on inheritance of real-life events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data instances are compared to identify relatives, then identification completeness is improved, but computational complexity becomes infeasible
Solution Approach 1:
The patent segments the comparison process into two stages: first comparing data instances at the window level (coarse-grained), then only comparing matching windows in detail (fine-grained). This hierarchical segmentation reduces the overall computational complexity while maintaining identification completeness.
Solution Approach 2:
The patent extracts and excludes overrepresented data strings that appear in many data instances but provide little discriminatory value for identifying relatives. By removing these common patterns from consideration, the system reduces computational waste while preserving the ability to identify true relationships.
2Measurement precision
If all matched data strings are counted for inheritance determination, then measurement completeness is improved, but measurement precision deteriorates due to overrepresented common patterns
Solution Approach 1:
The patent converts the harmful effect of overrepresented common patterns into a benefit by using them as exclusion criteria. These commonly occurring patterns, while reducing precision when included, are systematically identified and excluded, thereby improving the precision of inheritance determination by focusing only on discriminative patterns.
3Loss of information
If data comparison includes all data bits, then information completeness is improved, but processing time increases significantly
Solution Approach 1:
The patent segments data instances into multiple windows and performs comparison at the window level first. Only windows that match are then compared in detail, avoiding the need to process all data bits for every comparison. This segmentation dramatically reduces processing time while maintaining information completeness for relevant regions.
Solution Approach 2:
The patent applies partial action by comparing only the necessary portions of data instances - specifically, only matching windows are compared in detail rather than all data bits. This selective approach reduces processing time while maintaining sufficient information completeness for accurate inheritance determination.
Data Source
AI summary
A computing server may generate a catalog of overrepresented data strings from a database that stores a plurality of data instances. An overrepresented data string is a data string that matches to a number of data instances and the number exceeds a number threshold. The computing server may receive a target data instance that is to be compared to a related data instance. The computing server may determine one or more matched data strings that match between the target data instance and the related data instance. The computing server may compare the matched data strings to the catalog to exclude a subset of matched data strings that are matched to the overrepresented data strings. The computing server may determine a total length of the matched data strings excluding the subset of matched data strings that are matched to the overrepresented data strings.