From Data to Insights: Using Fuzzy Logic in Spatial Data Summarization with Fuzzy Spatial OLAP

The effectiveness of data knowledge acquisition is closely linked to the aggregation process, particularly in data warehouses where extensive data sets reside. Our study enhances the fuzzy spatial online analytical processing framework by inte-grating fuzzy aggregation, significantly improving the efficiency of intricate data queries. This approach streamlines data analysis by generating succinct, essential summaries and supports diverse, detailed queries by merging spatial OLAP concepts with fuzzy logic. The addition of fuzzy summaries plays a pivotal role in facilitating advanced knowledge extraction and accommodating various analytical needs. The innovation of our proposed frame-work lies in its utilization of fuzzy spatial aggregation methods, marking a substantial progression in sophisticated and efficient data handling. We conducted tests on actual datasets to assess the performance of our framework, revealing that our aggregate queries are notably more resource-efficient. Furthermore, the predictive aggregate queries yielded accurate results, demon-strating the effectiveness of our framework in terms of CPU utilization, memory efficiency, and execution time.