Data Scheduling Using Distance Function with Single-Pass Processing

Task scheduling is one of the most fundamental difficulties confronting numerous sectors, including education, healthcare, and industrial management. Scheduling quality has a direct impact on performance efficiency, resource utilization, and end user satisfaction. With the growing volume and diversity of data, there is a greater demand for intelligent systems that can handle the spatial and temporal complexity of jobs and resource allocation. As a result, modern data analysis and decision-making processes have become critical for ensuring workflow optimization while reducing time and space waste. This study proposes an innovative approach to task scheduling that combines multidimensional data analysis using Data Cubes (a business intelligence method that enables multidimensional data analysis) and spatial optimization using the Euclidean distance function. The main idea is to represent scheduling elements such as tasks, resources, times, and locations in a structured data cube, allowing for rapid execution of multidimensional queries and aggregations. By evaluating the Euclidean distance between jobs and available resources, the system may find the best match based on the shortest distance, ensuring efficient resource allocation. The use of a data cube to study scheduling patterns, resource utilization, and time allocation improves decision-making. The results indicate the model's effectiveness and scalability. Furthermore, merging business intelligence tools with engineering optimization methods creates a potential foundation for addressing complicated scheduling issues in dynamic situations.