HORIZONTAL AGGREGATIONS IN SQL TO PREPARE DATA SETS FOR DATA MINING ANALYSIS

The proposed study on horizontal aggregation in SQL for data mining preparation successfully demonstrates that SQL can be effectively used not only for querying but also for efficient data transformation. The research focused on converting vertically structured relational data into a horizontal format using three techniques: CASE, SPJ, and PIVOT. Let the input dataset be represented as: D={(ri​,cj​,vij​)}  where ri​ represents rows (records), cj​ represents categorical attributes, and vij​ represents corresponding values. The transformation process converts this vertical structure into a horizontal format: D′={(ri​,vi1​,vi2​,…,vik​)} where each category is mapped into a separate column. This transformation significantly improves data usability and reduces preprocessing effort for analytical tasks. From the performance evaluation, the computational efficiency of different methods can be expressed as: ·        CASE Method: O(n×k) ·        SPJ Method: O(nlogn) (depending on joins) ·        PIVOT Method: O(n) The experimental results confirm that the PIVOT method achieves the lowest execution time (0.0056 seconds), validating its linear time complexity behavior. In contrast, the CASE method shows increased computational overhead as the number of categories kkk increases. Additionally, the data transformation reduces the preprocessing complexity required for analytics. If traditional preprocessing requires multiple joins and grouping operations: Traditional​≈O(n×k+j) (where j represents join operations), the proposed approach reduces it to: Proposed​≈O(n) thus improving overall system efficiency.   The integration of visualization modules and AI-powered insights further enhances the practical applicability of the system. Features such as revenue analysis, category performance heatmaps, and automated insight generation demonstrate how horizontally aggregated data supports efficient decision-making. Overall, the study concludes that horizontal aggregation using SQL is a powerful, scalable, and efficient approach for preparing datasets for data mining and business intelligence applications. The combination of theoretical efficiency and practical performance validates the effectiveness of the proposed system.

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