그래프 데이터베이스 모델을 이용한 효율적인 부동산 빅데이터 관리 방안에 관한 연구

Real estate data can be big data. Because the amount of real estate data is growing rapidly and real estate data interacts with various fields such as the economy, law, and crowd psychology, yet is structured with complex data layers. The existing Relational Database tends to show dificulty in handling various relationships for managing real estate big data, because it has a fixed schema and is only verticaly extendable. In order to improve such limitations, this study constructs the real estate data in a Graph Database and verifies its usefulnes. For the research method, we modeled various real estate data on MySQL, one of the most widely used Relational Databases, and Neo4j, one of the most widely used Graph Databases. Then, we colected real estate questions used in real life and selected 9 diferent questions to compare the query times on each Database. As a result, Neo4j showed constant performance even in queries with multiple JOIN statements with inferences to various relationships, whereas MySQL showed a rapid increase in its performance. Acording to this result, we have found out that a Graph Database such as Neo4j is more eficient for real estate big data with various relationships. We expect to use the real estate Graph Database in predicting real estate price factors and inquiring AI speakers for real estate.