Practical Solutions for Data Consistency and Query Performance in Graph Database and Search Engine Integration
Shriman K. Arun, Sangeetha Krishnan, B. Mohnish Karthikeyan, H. G. Leerish Arvind, Narayanan Ganesh · 2025
The integration of graph databases with search engines is essential for deriving information from complex interconnected data, but it presents significant challenges, particularly in maintaining data consistency and optimizing query performance. Two key challenges—data consistency anomaly and query performance anomaly—can severely undermine the effectiveness of this integration. Data consistency anomaly occurs due to synchronization delays between the graph database and search engine, leading to inconsistent or outdated data being retrieved, which compromises the accuracy of analytical outcomes. Query performance anomaly, on the other hand, emerges when graph traversal queries are executed inefficiently, resulting in slower response times and hindering the retrieval of meaningful insights from interconnected datasets. To address these challenges, this research proposes a framework that integrates real-time synchronization mechanisms and query optimization techniques. Using the Hybrid Synchronization Protocol (HSP) and Proof-of-Authority (PoA) consensus, the framework ensures real-time data updates and validation, significantly improving data consistency. Additionally, it employs Latent Dirichlet Allocation (LDA) for dynamic topic modeling and Weighted Fair Queuing (WFQ) for query scheduling, enhancing query relevance and response time. The Ant Colony Optimization (ACO) algorithm is also utilized to manage cache updates and reduce synchronization latency. Evaluation of the framework, implemented on a Neo4j graph database with a dataset of 1,000,000 nodes and 2,000,000 relationships, demonstrated notable performance improvements. Query response time improved by 29%, data consistency errors were reduced by 90%, synchronization latency decreased by 23%, query relevance increased by 28%, and cache update latency improved by 33%. These results highlight that the proposed framework effectively addresses the challenges of data consistency and query performance, offering a scalable and practical solution for organizations dealing with large-scale, interconnected data environments.