Real-Time Data Monitoring and Anomaly Detection with AI: a Comprehensive Overview

EasyChair Preprint no. 13212

Abstract

Real-time data monitoring and anomaly detection have become vital tasks in various domains, including finance, cybersecurity, industrial processes, and healthcare. With the explosion of data generation and the increasing complexity of systems, traditional manual approaches to data monitoring and anomaly detection are often insufficient and impractical. As a result, the integration of Artificial Intelligence (AI) techniques has emerged as a powerful solution to address these challenges, enabling automated, efficient, and accurate detection of anomalies in real-time data streams.

This paper provides a comprehensive overview of real-time data monitoring and anomaly detection techniques employing AI methodologies. Firstly, we discuss the fundamental concepts and challenges associated with real-time data monitoring and anomaly detection. We highlight the significance of timely detection, the need for continuous monitoring, and the potential consequences of undetected anomalies.

Subsequently, we delve into various AI-based approaches utilized for real-time data monitoring and anomaly detection. These include machine learning algorithms, deep learning models, and ensemble methods. We explore their strengths, weaknesses, and suitability for different contexts, such as structured and unstructured data, batch processing, and stream processing.

Keyphrases: AI, anomaly detection, deep learning, machine learning, Real-time data monitoring

@Booklet{EasyChair:13212,
  author = {Harold Jonathan and Edwin Frank},
  title = {Real-Time Data Monitoring and Anomaly Detection with AI: a Comprehensive Overview},
  howpublished = {EasyChair Preprint no. 13212},

  year = {EasyChair, 2024}}