This paper addresses the problem of excessive computational costs, time overhead, and inefficient utilization of processing resources in the analysis of KPI (Key Performance Indicators) data from the perspective of computer science and systems analysis. The study considers the complete lifecycle of KPI data, including acquisition, storage, preprocessing, and analytical processing, within a formalized systems framework. Mathematical models are proposed to describe cost functions, time complexity, and resource consumption associated with KPI data processing workflows. Based on these models, algorithmic solutions are developed using incremental computation, hierarchical data aggregation, and multi-objective optimization techniques aimed at minimizing computational load, memory usage, and processing latency. The proposed algorithms are analyzed in terms of computational complexity and scalability and are experimentally evaluated in comparison with conventional batch-oriented approaches. Experimental results demonstrate a significant reduction in processing time, computational resource consumption, and overall operational costs. The findings confirm the applicability of the proposed models and algorithms for optimizing KPI-driven decision-support systems in public administration, higher education management, and large-scale corporate information systems.
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