Real-Time Predictive Analytics: A Framework for Dynamic Decision Intelligence in Event-Driven Architectures

Sudhakar Reddy Vyza

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025, vol. 11, issue 1, 2286-2294

Abstract: This article presents a comprehensive examination of real-time predictive analytics systems, focusing on their integration with modern data pipeline architectures for enhanced decision intelligence. The article proposes a novel framework that combines event-driven processing with adaptive machine learning models to enable dynamic decision-making in complex environments. The article investigates the architectural components necessary for processing high-velocity data streams while maintaining prediction accuracy and system reliability. Through multiple case studies across financial markets and supply chain operations, the article demonstrates the framework's effectiveness in supporting real-time decision-making processes. The article highlights the significance of optimized pipeline architectures in reducing latency while maintaining model accuracy, contributing to both theoretical understanding and practical implementation of real-time predictive systems. The article also addresses critical challenges in scalability, fault tolerance, and model adaptation, providing insights for future developments in the field of real-time analytics.

Keywords: Real-time analytics; Predictive modeling; Event-driven architecture; Stream processing; Decision intelligence (search for similar items in EconPapers)
Date: 2025
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT251112233
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT251112233 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT251112233/CSEIT251112233 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v11:y2025:i1:id:897

DOI: 10.32628/CSEIT251112233

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma ().