Identifying Technical Debt in Database Normalization Using Association Rule Mining

In previous work, we explored a new context of technical debt that relates to database normalization design decisions. We claimed that database normalization debts are likely to be incurred for tables below the fourth normal form. We proposed a method to prioritize the tables that should be normalized based on their impact on data quality and performance. In this study, we propose a framework to identify normalization debt items (i.e. tables below the fourth normal form) by mining the data stored in each table. Our framework makes use of association rule mining to discover functional dependencies between attributes in a table, which will help determine the current normal form of that table and reveal debt tables. To illustrate our method, we use a case study from Microsoft, AdventureWorks database. The results revealed the applicability of our framework to identify debt tables.