Processing and Analysis of Data from Fiscal Electronic Cash Registers in the Context of IoT and Big Data

Abstract

This paper proposes a framework for processing and analysing fiscal data from electronic cash registers using Big Data and IoT. The system is designed for handling large volumes of transactional data by integrating data collection, cleaning, transformation, and analysis through a modular architecture based on microservices, distributed messaging, and both relational and NoSQL databases. Before carrying out the data analysis, the missing or inconsistent values are addressed using regression models, which enhances data quality. In order to contribute to anomaly detection in fiscal activities, the proposed platform supports statistical analysis, time series analysis, and pattern recognition. Real-world data-based tests revealed that the proposed technological solution can help the tax authorities track data compliance and increase the effectiveness of fiscal data operations.