Query-based denormalization using hypergraph (QBDNH): a schema transformation model for migrating relational to NoSQL databases

Query-based denormalization using hypergraph (QBDNH): a schema transformation model for migrating relational to NoSQL databases

연구 분야: Databases

학회: Knowledge and Information Systems

With the emergence of NoSQL databases, many large applications have migrated from relational databases (RDB) due to their superior flexibility and performance. Database migration from RDB to NoSQL databases involves schema transformation and data migration, which is not straightforward. The challenge lies in that RDB stores data in normalized form, whereas NoSQL supports denormalization. To address the challenge of schema transformation, this paper proposes a model called query-based denormalization using hypergraph (QBDNH) from RDB to the NoSQL database. The model takes the inputs from existing relational tables and queries and transforms them into the denormalized NoSQL model using hypergraphs. The approach overcomes limitations like complex relationship representation and data access pattern coverage of existing graph-based denormalization techniques. The proposed model reduces the overall time, cost, and effort needed to transform the schema manually. To validate the effectiveness of QBDNH, the experiments are conducted on the TPC-H dataset, and the performance of QBDNH is compared to existing graph-based denormalization models such as TLD, CLDA, and Kuszera. The evaluation is carried out in two parts: the first part analyzed the query speedup factor, while the second part measured efficiency improvement based on query pipeline execution. The results revealed that QBDNH achieved a notable query performance improvement with speedup factors of 1.29, 1.35, and 1.40 compared to existing TLD, CLDA, and Kuszera models. Furthermore, QBDNH significantly enhanced pipeline utilization compared to TLD and Kuszera.