Data stream analysis: Foundations, major tasks and tools

TLDR

The rapid growth of IoT devices and social networks generates continuous data streams, and while machine learning can extract valuable information, the evolving nature and high arrival rates of these streams make storage and knowledge extraction challenging. This survey aims to review the research constraints and current state‑of‑the‑art in data stream analysis. It provides an updated overview of recent contributions to stream mining tasks, including classification, regression, clustering, and frequent pattern discovery. The article is categorized under Fundamental Concepts of Data and Knowledge: Key Design Issues in Data Mining and Motivation and Emergence of Data Mining.

Abstract

Abstract The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social networks, along with the evolution of technology in different domains, lead to a rise in the volume of data generated continuously from multiple systems. Valuable information can be derived from these evolving data streams by applying machine learning. In practice, several critical issues emerge when extracting useful knowledge from these potentially infinite data, mainly because of their evolving nature and high arrival rate which implies an inability to store them entirely. In this work, we provide a comprehensive survey that discusses the research constraints and the current state‐of‐the‐art in this vibrant framework. Moreover, we present an updated overview of the latest contributions proposed in different stream mining tasks, particularly classification , regression , clustering , and frequent patterns . This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining