Real-Time Data Processing: Frameworks, Machine Learning Integration, and Traffic Analysis Using Computer Vision

Authors: Jagjeet Singh

DOI: https://doi.org/10.37082/IJIRMPS.v12.i4.230767

Short DOI: https://doi.org/gt4pt5

Country: India

Full-text Research PDF File:   View   |   Download

Abstract: Real-time data processing plays a vital role in modern applications, providing imme-diate insights across various domains. This paper reviews current frameworks, explores machine learning integration, and highlights challenges such as latency reduction and data security. Through a case study on real-time traffic analysis using computer vision, we demonstrate the effectiveness of real-time analytics and propose future research di-rections.

Keywords: Real-Time Data Processing, Stream Processing Frameworks, Apache Kafka, Apache Flink, Apache Storm, Machine Learning Integration, Traffic Analysis, Computer Vision, Latency Reduction, Data Security, Scalability, Event Sourcing, Log Aggregation, Complex Event Processing, ETL, Predictive Analytics, Fault Tolerance, Real-Time Analytics, Throughput, Video Stream Processing, Object Detection

Paper Id: 230767

Published On: 2024-07-18

Published In: Volume 12, Issue 4, July-August 2024

Cite This: Real-Time Data Processing: Frameworks, Machine Learning Integration, and Traffic Analysis Using Computer Vision - Jagjeet Singh - IJIRMPS Volume 12, Issue 4, July-August 2024. DOI 10.37082/IJIRMPS.v12.i4.230767

Share this