Genealogy Data Inheritance Tracing via Matched Strings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Identifying relatives in large-scale genealogy databases is challenging due to the sheer volume of data and the computational complexity of comparing datasets, especially when real-world events leading to data inheritance are not documented.
Innovation Solution
A computer-implemented method that receives a target data instance, scans for related instances with matched data strings, determines data group labels using a hidden Markov model, and stores data group information to estimate common real-life entities, including displaying geographical maps for matched data strings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large-scale database with billions of data records is used to store genealogy information, then the database can accommodate more users and data, but the computational complexity and time required to identify relatives increases significantly
Solution Approach 1:
The patent segments the large-scale database into multiple data centers, each handling a portion of the genealogy data. The system divides the search space by distributing data across multiple centers and using coordinated search strategies, thereby reducing the computational burden on any single system while maintaining the ability to search through billions of records.
Solution Approach 2:
The patent introduces intermediary components including proxy servers that manage search requests, coordinate between multiple data centers, and aggregate results. These intermediaries handle the complexity of distributed searching, allowing the system to scale to billions of records without proportionally increasing computational complexity at any single point.
2Ease of operation
If traditional dataset comparison methods are used to identify relatives, then the approach is straightforward, but the method becomes computationally infeasible when comparing large numbers of datasets with many data bits
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing genealogy data before actual relative searches. Data is organized into structured formats with pre-computed relationships and patterns stored, allowing the search system to quickly retrieve and compare only relevant portions of datasets rather than performing exhaustive comparisons of all data bits.
Solution Approach 2:
The patent implements partial action by comparing only specific portions of datasets that are most relevant for identifying relatives, rather than comparing all data bits. The system identifies and focuses on key inheritance patterns and data segments, performing excessive searching in targeted areas while avoiding unnecessary comparisons elsewhere, thereby improving overall search efficiency.
3Reliability
If the system searches through all data instances to find relatives, then comprehensive results can be obtained, but the time and computational resources required become prohibitive
Solution Approach 1:
The patent implements feedback mechanisms where the search system continuously evaluates results and adjusts its search strategy based on findings. As relatives are identified and their data patterns understood, the system refines its search criteria and focuses subsequent searches on promising areas, reducing the need to exhaustively search all data instances while maintaining comprehensive results.
Solution Approach 2:
The patent employs dynamic search strategies that adapt during the search process. The system adjusts search parameters, data access patterns, and comparison criteria based on intermediate results and computational resource availability, allowing it to maintain reliability in identifying relatives while optimizing search time and resource usage throughout the process.
Data Source
AI summary
A computing server may receive a target data instance. The computing server may scan through a data store to identify a related data instance that shares one or more matched data strings with the target data instance. The computing server may determine one or more windows of data locality to which the one or more matched data strings belong. The computing server may determine one or more data group labels for the one or more windows of data locality that includes the one or more matched data strings. The computing server may store data group information of the matched data strings between the target data instance and the related data instance.