Efficient Parent‑Child Relationship Deduplication Using Hashset Caching

Introduction: In development, deduplication scenarios arise frequently; with large datasets, inefficient algorithms cause slow performance and errors. This paper introduces an efficient deduplication algorithm based on parent‑child relationships.

Problem: Validating parent‑child chains repeatedly computes the same intermediate steps, leading to exponential growth in verification steps as dataset size and chain length increase.

Solution: Cache validated intermediate results in a hashset; each element’s validation checks its parent in the set, allowing O(1) lookup and eliminating duplicate work.

Detailed method: For an element X, verify its parent P via function f(P)=X; confirm P exists in hashset (or is root); then add X to hashset. This reduces total steps from r = (1+m)·m·(n/m)/2 to r = (m+2·(m‑1))·(n/m), where n is element count and m average chain length.

Benefits: With n=100 and m=5, steps drop from 300 to 280 (6% gain); with m=20, steps drop from 1050 to 295 (71% gain); with n=1000, m=200, steps drop from 100,500 to 2,995 (97% gain), showing improved scaling.

Alternative approaches: Use hashmap for richer cached data, employ distributed/local file storage with custom deduplication, or mark each element as an object after validation.