A SURVEY OF STREAM DATA MINING

TLDR

Data streams arise in real‑time surveillance, telecommunications, sensor networks, and other dynamic environments, creating massive, high‑rate, transient data that must be processed online, posing significant storage, querying, and mining challenges. This survey presents the theoretical foundations of data stream analysis and outlines future research directions. The paper reviews mining data stream techniques, focusing on extracting knowledge structures and patterns from continuous, non‑stopping information streams. Index terms: data streams, data mining, review.

Abstract

Abstract – At present 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. In this paper, we present the theoretical foundations of data stream analysis and identify potential directions of future research. Mining data stream techniques are being critically reviewed. Index terms —data streams, data mining, review 1. INTRODUCTION Recently a new class of emerging applications has become widely recognized: applications in which data is generated at very high rates in the form of transient