Overview of data collection and analysis methods in intelligent information processing systems

Abstract

This article examines modern methods of data collection and analysis in intelligent information processing (IIP) systems. A review of available services, their features, advantages, and disadvantages across various fields such as marketing, jurisprudence, medicine, and military affairs is provided. Special attention is paid to the integration of data from messengers and public channels. The article proposes an approach that combines traditional data collection services with sources such as Telegram, Facebook, and YouTube to build a more comprehensive and representative informational framework.

The results of experimental research demonstrate that even non-standard information sources can be effectively integrated into analytical systems for automated detection of patterns, trends, and anomalies. A conceptual model is proposed, which integrates hyper-automation with IIP to improve the efficiency of data collection, processing, and analysis. Experimental research confirms the feasibility of implementing such solutions, particularly for detecting disinformation, forecasting threats, and automating routine tasks.

The article highlights the prospects for developing hyper-automated systems in the context of a rapidly evolving information environment.