Analysis of Consumer Behavior Using an Intelligent Multi-Source System

Abstract

Purpose: This work aims to develop an innovative system that analyzes multi-source data and human behavior, ultimately creating and sharing improved procedures and solutions. It focuses on building an IT system prototype for behavior analysis, optimizing the data mining process, and generating innovative business processes. Design/Methodology/Approach: The application aims to optimize processes, analyze data, and reveal relationships between data and processes. Business models will be created using external data, data warehouses (such as ERP systems), and data from online resources (web mining). A process database will support computational intelligence algorithms, with an agent responsible for gathering online data. New data management methods were developed and implemented, while algorithms were designed for efficient web data searching. The system will leverage artificial neural networks, statistical and stochastic methods, fuzzy sets, genetic algorithms, and combinations to build an intelligent computing system. Findings: The innovative system will contribute new data management methods and algorithms for web data searching and analysis. The algorithms will advance methods and concepts for capturing, transmitting, collecting, and extracting information while providing suitable data presentation formats. Practical Implications: The insights from this system have the potential to revolutionize the way businesses identify and optimize new processes, generate innovative business models, and strengthen their decision-making. By comprehensively analyzing multi-source data, this system can inspire and motivate professionals in the field of data analysis and process optimization. Originality/Value: This research is at the forefront of developing and implementing a system for analyzing multi-source data and human behavior. By using cutting-edge techniques such as artificial neural networks, statistical and stochastic methods, fuzzy sets, and genetic algorithms, this work provides an intelligent and robust framework for mining data and optimizing business processes, which is sure to intrigue and interest academic researchers, data analysts, and business professionals in the audience.