Abstract: This research paper presents a pioneering approach to incident processing within the realm of business intelligence (BI) by harnessing the potency of artificial intelligence (AI) techniques, with a specific emphasis on association analysis. The efficient handling of incidents is essential for organizations to promptly address issues and make informed choice based on insights extracted from their data. However, traditional incident processing methods often fall short in effectively capturing and leveraging the wealth of information contained within the data. To overcome these obstacles, this paper proposes an AI-based framework that combines machine learning, natural language processing, and association analysis algorithms to enhance incident handling, automate resolution processes, and enable proactive decision-making.

The proposed methodology encompasses a comprehensive workflow that encompasses data ingestion, pre-processing, and analysis stages. It integrates AI techniques to detect, classify, and prioritize incidents, leveraging advanced algorithms to identify patterns and correlations through association analysis. Through extensive experimental evaluation, the effectiveness and performance of the AI-driven incident processing framework are examined, providing insights into its superiority compared to traditional approaches. Real-world applications and case studies highlight the practical implications and benefits of this framework, including enhanced operational efficiency, cost reduction, and improved decision-making. The research findings outlined in this paper contribute to the advancement of incident processing in BI, serving as a valuable resource for researchers, practitioners, and decision-makers seeking to leverage AI for more proactive and data-driven incident management.

Keywords: Incident processing, Business intelligence, Artificial intelligence, Association analysis, Machine learning

| DOI: 10.17148/IARJSET.2023.10781