Genealogy data design appropriate for family relationship retrieving

Area of Science:

• Computer Science
• Bioinformatics
• Graph Theory

Background:

• Genealogical data is complex and requires efficient methods for storage and relationship analysis.
• Existing data structures may not adequately capture the nuances of family connections.
• Representing family trees computationally is crucial for genetic and historical research.

Purpose of the Study:

• To introduce a novel, stand-alone data structure for representing genealogical information.
• To enable the storage and recognition of a wide range of family relationships.
• To develop and classify algorithms for efficient genealogy data retrieval.

Main Methods:

• Representing genealogies as finite, connected graphs with nodes for individuals.
• Using ordered pairs (arcs) for parent-offspring relationships and unordered pairs (edges) for twinships.
• Classifying retrieval algorithms into vertical, lateral, and parallel categories.

Main Results:

• The proposed data structure efficiently stores essential family relationships.
• It allows recognition of relationships such as ancestor-descendant, siblings, twins, cousins, and spouses.
• Combined vertical and lateral algorithms can identify any relative, direct or collateral.
• A parallel algorithm is specifically designed for identifying multiple births (twins, triplets, etc.).

Conclusions:

• The graph-based data structure provides a robust framework for genealogical data.
• The developed algorithms offer effective methods for querying and analyzing family connections.
• This approach enhances the computational handling of complex family structures.