Incremental discovery of functional dependencies using partitions
Shyue-Liang Wang, Ju-Wen Shen, Tzung‐Pei Hong · 2002
The discovery of functional dependencies (FDs) in relational databases is an important data mining problem. Most current work assumes that the database is static, and a database update requires rediscovering all the FDs by scanning the entire old and new database repeatedly. In this work, we present an efficient data mining algorithm to incrementally discover all FDs in the presence of a new set of tuples added to an old database. Based on the concept of tuple partitions and the monotonicity of FDs, we avoid re-scanning of the database and thereby reduce the computation time. The computational complexity of the proposed algorithm is analyzed. A comparison the with the pair-wise comparison-based incremental approach is also presented. The results show that an improved computation time is achieved, while extra space is required for partitions by our approach.