File(s) under permanent embargo
Mining data streams: a review
journal contribution
posted on 2005-06-01, 00:00 authored by M M Gaber, Arkady ZaslavskyArkady Zaslavsky, S KrishnaswamyThe 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.
History
Journal
ACM SIGMOD recordVolume
34Issue
2Pagination
18 - 26Publisher
Association for Computing MachineryLocation
New York, N.Y.Publisher DOI
ISSN
0163-5808Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2005, ACMUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC