How the Quantum-inspired Framework Supports Keyword Searches on Multi-model Databases

With the trend of increasing vendors to develop various multi-model databases, people have reaped benefits from using a single and unified platform to manage both well-structured and NoSQL data. However, it causes a steep learning curve of mastering a multi-model query language for the specific multi-model database, not to mention various languages for different databases. Therefore, this research discusses the motivations of performing keyword searches on multi-model databases and then presents our current research. Methodologically, we attempt to use the quantum-inspired framework to query and explore multi-model databases. Firstly, we apply non-classical probabilities to estimate the relevance between a keyword query and candidate answers for guaranteeing getting good accuracy. Then we use the Principle Component Analysis (PCA) method to optimize the quantum language model for capturing good scalability. Finally, experiments show that our approaches are effective and our framework outperforms the state-of-the-art approaches.