2005/06/01 by Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy · 2 citations
Computer Science · #Data Stream Mining Techniques #Time Series Analysis and Forecasting #Data Management and Algorithms #Computer science #Data stream mining #Field (mathematics) #Data mining #Data science #State (computer science) #Range (aeronautics) #Data stream
paper · doi:10.1145/1083784.1083789
openalex publication_date 2005/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. 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. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.