Genealogical Information Search by Using Parent Bidirectional Breadth Algorithm and Rule Based Relationship

Genealogical information is the best histories resources for culture study and cultural heritage. The genealogical research generally presents family information and depict tree diagram. This paper presents Parent Bidirectional Breadth Algorithm (PBBA) to find consanguine relationship between two persons. In addition, the paper utilizes rules based system in order to identify consanguine relationship. The study reveals that PBBA is fast to solve the genealogical information search problem and the Rule Based Relationship provides more benefits in blood relationship identification.

💡 Research Summary

The paper addresses the challenge of identifying consanguine relationships between two individuals within large genealogical databases, a problem that becomes increasingly complex due to population growth, frequent household migrations, and the sheer volume of family records. Traditional search methods such as single‑direction Breadth‑First Search (BFS) or even bidirectional BFS suffer from exponential growth in the number of explored nodes because each node may have many child links (denoted V). To overcome this, the authors propose the Parent Bidirectional Breadth Algorithm (PBBA). PBBA restricts movement to parent links only (father and mother) and initiates simultaneous searches from both query individuals. Consequently, at each depth level only two successor nodes are considered per active frontier, yielding a worst‑case node count of 2 × Lmax for a single‑direction search and 4 × Lmax for the bidirectional version, where Lmax is the maximum depth needed to reach a common ancestor. This reduction dramatically lowers both time and space complexity compared with conventional BFS, especially when V is large.

After PBBA determines that two persons share a blood relationship, the system must translate the abstract graph connection into a human‑readable kinship term (e.g., “grandfather”, “cousin”). For this purpose the authors develop a rule‑based relationship identification module. The module first classifies the two nodes as belonging to the “descendant generation” (the deeper node) or the “ancestor generation” (the shallower node). It then uses the generation distance (the number of edges between the nodes) together with the gender of each individual to select the appropriate kinship label from a predefined rule table (illustrated in the paper’s Figures 5 and 6). The rules are expressed in English but are designed to be adaptable to other cultural naming conventions.

The experimental evaluation consists of generating synthetic family trees with varying numbers of families (from two up to five) and a configurable number of children per couple. Random pairs of individuals are selected and processed by three algorithms: standard BFS, bidirectional BFS, and the proposed PBBA. The authors report both the number of nodes visited and the elapsed CPU time. Results show that PBBA consistently visits far fewer nodes and runs faster than the other two methods; the advantage grows as the branching factor V increases. Graphical plots (Figure 10) and a comparative table (Table 1) illustrate this trend. In addition, the rule‑based module correctly assigns kinship terms in the majority of cases, with only two types of errors observed: false positives where unrelated individuals were mistakenly linked, and mislabeling of the exact relationship. Manual verification using a “family identifier” column confirmed that the overall correctness rate exceeds 98 %.

The contributions of the paper are threefold. First, it introduces a novel search strategy (PBBA) that leverages parent‑only, bidirectional traversal to achieve linear‑in‑depth complexity for genealogical queries, making real‑time relationship discovery feasible in massive databases. Second, it couples this efficient search with a deterministic, rule‑based system that translates graph paths into culturally meaningful kinship terms, thereby bridging the gap between algorithmic output and user‑friendly information. Third, the authors provide a thorough theoretical analysis of algorithmic complexity together with empirical validation, demonstrating that the proposed approach outperforms existing methods both in speed and accuracy.

Limitations are acknowledged. The current model assumes only biological parent‑child links; it does not handle adoption, step‑relationships, or non‑blood marriages, which are common in many societies. The rule set is based on English‑language kinship terminology and would require extension and validation for other linguistic or cultural contexts. Future work suggested includes integrating non‑blood relationship edges into the graph, employing machine‑learning techniques to infer implicit connections, and expanding the rule engine to support multilingual kinship vocabularies.

In summary, the paper presents a practical and theoretically sound solution for fast genealogical relationship search and precise kinship labeling, offering significant benefits for applications such as cultural heritage management, legal inheritance processing, medical genealogy, and large‑scale demographic research.