LAUSR

Rural governance relies on distinct geographical, population, and fundamental service attributes for deploying digital construction and operation modes. The digital platform for rural governance includes surveying, identifying, and fulfilling the demands through application-specific user interactions. This article discloses a Modular Data Representation Method (MDRM) for improving the data semantics in digital platforms. The proposed method improves the presentation, analysis, and interaction in the governance process through requirements-based intelligent processing. The processing is performed based on the data organization as recommended by the regression learning paradigm. In this paradigm, the forward regression for data representation and service delegations are linearly analyzed. Based on the processing, the service requirement is met with big data availability. Therefore, the representation recommendations and data-driven analysis are provided through digital platform implications, improving the service availability. This is consistently provided based on the regressive outputs for data analysis. Therefore, the proposed method’s performance is analyzed using the metrics analysis time, data processing rate, and unavailability.