StreamDrift: A Unified Model for Detecting Gradual and Sudden Changes in Data Streams

Main Article Content

Tabssum Khan
Arkan Ahmed Hussein
Ahmad M. Hussein Shabani

Abstract

In the era of big data, the ability to detect changes in data streams is critical for maintaining the accuracy and reliability of real-time analytics. This research introduces StreamDrift, a unified model designed to identify both gradual and sudden changes in data streams. The primary objectives of this study are to develop an efficient and adaptive method for change detection and to evaluate its performance across various domains. The proposed model leverages advanced machine learning algorithms, specifically tailored for continuous data flow analysis, to detect anomalies and trends with high precision. To validate the effectiveness of StreamDrift, extensive experiments were conducted using a comprehensive dataset comprising financial transactions, network traffic data, and environmental sensor readings. Key metrics used to measure the model's performance include detection accuracy, false positive rate, detection delay, and computational efficiency. The findings indicate that StreamDrift outperforms traditional change detection methods by providing more timely and accurate detection of both gradual and sudden changes. The applicability of StreamDrift spans multiple fields, including financial monitoring, where it can detect fraudulent activities; network security, where it identifies potential threats in real time; and environmental sensing, where it monitors changes in environmental conditions. The integration of adaptive mechanisms within StreamDrift allows for continuous learning and adjustment, ensuring robustness and reliability in diverse and dynamic data environments.In conclusion, StreamDrift presents a significant advancement in data stream analysis, offering a versatile and effective solution for real-time change detection across a wide range of applications. This study highlights the model's potential to enhance decision-making processes and improve the overall efficiency of data-driven operations.

Article Details

How to Cite
[1]
Tabssum Khan, Arkan Ahmed Hussein, and Ahmad M. Hussein Shabani, “StreamDrift: A Unified Model for Detecting Gradual and Sudden Changes in Data Streams”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 5, pp. 58–65, May 2024.
Section
Research Articles

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