<![CDATA[Efficient data transmission is a critical aspect of modern web applications, particularly in scenarios involving large tabular datasets rendered through server-side DataTables. This study proposes an adaptive categorical dictionary approach to reduce the payload size transmitted between the server and client. The strategy leverages the high frequency of categorical values within datasets by encoding them into shorter symbolic representations stored in a dynamically generated dictionary. The dictionary is constructed on the server during the initial request and maintained throughout the session, while the client retains a synchronized copy in memory. The research utilizes a publicly available college student dataset containing 1,545 records, focusing on columns with repetitive categorical values such as major, gender, and enrollment status. Experimental simulations were conducted under varying DataTables page lengths (10, 25, 50, and 100) to evaluate the impact of dictionary encoding on request and response payload sizes. Results demonstrate consistent payload reductions across all configurations, with significant improvements observed in larger page lengths—exceeding 12% in some cases. These findings confirm the effectiveness of the adaptive dictionary in minimizing response payloads, thereby improving communication efficiency in AJAX-based data-driven applications. The approach maintains compatibility with native PHP and JavaScript implementations and introduces minimal overhead, making it suitable for integration into existing server-side processing architectures.]]>
Copyrights © 2025
