The exponential growth and diversity of data in business environments present significant challenges for analysis and decision-making. Organizations are confronted with a wide array of data formats and an ever-increasing volume of information, which complicates the extraction of actionable insights. Furthermore, the volatility and unpredictability of business needs necessitate swift and strategic adaptations, as requirements evolve rapidly. These complexities underscore the urgent need for innovative approaches, technologies, and models to effectively ingest, process, and analyze data. Beyond traditional statistical methods, advanced solutions are required to uncover new knowledge that can drive business growth and optimization.

Artificial Intelligence (AI) has emerged as a transformative force in addressing these data challenges. In recent years, AI has moved from conceptual exploration to practical application across numerous business domains. However, its integration into business processes has been selective, with a gradual absorption into various management aspects. AI's impact is particularly evident in areas such as automation of production processes, investment and risk management, business communication strategies, customer relationship management, operational support, cybersecurity, and advanced data analytics.

AI tools, such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or Model Context Protocol (MCP), are now recognized as essential instruments for supporting intensive data search, preparation, and analysis in a broad range of domains, including the most traditional business sectors. These tools offer sophisticated models and techniques that enable organizations to handle data in a highly efficient, rapid, and streamlined manner, thereby enhancing competitiveness and informing decision-making processes at operational, tactical, and strategic levels.

This workshop aims to highlight and promote cutting-edge research on innovative techniques and models for data storage, processing, and analysis using disruptive approaches to knowledge engineering and decision-making systems. Emphasizing the use of AI models and techniques, the workshop will focus on solutions that incorporate the latest advancements in Generative AI, showcasing their potential to address complex data challenges and contribute to business advancement.



List of topics of interest

Topics of interest include (but are not limited to):

• Artificial Intelligence Applications in Business Intelligence and Data Analytics

• Integration of Business Intelligence, Business Analytics, and Data Science

• Data Analytics, Data Mining, and Computational Intelligence Techniques

• Generative Artificial Intelligence and Its Applications in Business Intelligence

• Intelligent Agents and Bots for Business Intelligence and Data Analytics

• Design and Development of Modern Data Architectures: Data Lakes, Data Warehouses, Data Lakehouses, Data Meshes, and Data Vaults

• Advanced Data Models and Architectures for Intelligent Analytics Systems

• Data Quality, Curation, Provenance, Security, Cataloging, and Governance Enhanced by Generative AI

• Intelligent Dashboards for Analytical Key Performance Indicators

• Ontology Learning and Its Role in Data Analytics

• High-Performance Query Processing and Analysis for Big Data

• Sentiment and Opinion Mining in Unstructured Data

• AI Applications in Learning, Finance, Marketing, Banking, Medicine, Industry, and Services

• Real-Time Data Processing and Stream Analytics in Business Intelligence

• Ethical Considerations and Bias Mitigation in AI-Driven Business Intelligence