Publication

Abstract :

In contrast to the conventional B-Tree structure, this study's innovative Hybrid grid index structure incorporates R-Tree to process queries including location-based spatial data more quickly. Materials and Methods: To train and evaluate the proposed prediction model, which comprises 9999 numbers of sold models of automobiles with 9 features, we employ a record of gathering of four wheeler sales report dataset from various places of India. To evaluate the model's performance in handling spatial data queries in location-based services, two categories are employed: the R-Tree approach, which combines a novel hybrid grid indexing technique with the B-Tree algorithm, and the N=10 sample iterations of the B-Tree algorithm. When determining the sample size and doing t-test analysis, G-power is taken to be 80. Result: While the B-Tree indexing model achieves an accuracy of 65.04 percent, the R-Tree combined with the innovative hybrid grid structure has the ability to achieve an accuracy of 89.60 percent. R-Tree and B-Tree differ significantly from each other with a p-value less than 0.05. At p=0.288, the study is statistically significant. Conclusion: The R-Tree combined hybrid grid index structure outperforms the B-Tree indexing technique in terms of performance and significance when it comes to enhancing the query response time in query processing on geographical data. © 2025 Author(s).