An analytical framework for data stream mining techniques based on challenges and requirements

TL;DR

This paper presents a theoretical framework for data stream mining, addressing challenges like real-time processing, concept drift detection, and the application of various techniques in dynamic data environments.

Contribution

It introduces an analytical framework that classifies and analyzes data stream mining techniques based on key challenges and requirements.

Findings

Framework categorizes techniques by challenges

Highlights importance of fast, adaptive mining methods

Provides theoretical foundations for data stream analysis

Abstract

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.