Trend Visualization and Storage Optimization for Powerlifting Data on Oracle Database

The volume of powerlifting performance data has grown rapidly, presenting new opportunities and challenges for sport scientists and database practitioners. This paper presents an exploration of the OpenPowerlifting dataset, comprising over three million competition results collected worldwide, by (1) visualizing trends in performance and participation, and (2) evaluating optimizations to support efficient data visualization on Oracle Database 12c. First, we visualize how viewing the dataset through stratification like age class, used equipment type, competition year and competitor nationality reveals progression in powerlifting outcomes. Second, we benchmark standard SQL queries against alternative storage architectures: baseline tables versus tables employing basic compression, reduced column sets, and table partitioning. Our results show that indexes offer limited benefit for our data visualization workloads, partitioning and table compression yield significant improvements in query execution times and required storage space. Our work explores key performance patterns and practical database optimizations that help researchers efficiently study and analyze vast amounts of powerlifting competition data.