File(s) not publicly available
Towards an adaptive approach for mining data streams in resource constrained environments
conference contribution
posted on 2004-01-01, 00:00 authored by M M Gaber, Arkady ZaslavskyArkady Zaslavsky, S KrishnaswamyMining data streams in resource constrained environments has emerged as a challenging research issue for the data mining community in the past two years. Several approaches have been proposed to tackle the challenges of limited capabilities for small devices that generate or receive data streams. These approaches try to approximate the mining results with acceptable accuracy and efficiency in space and time complexity. However these approaches are not resource-aware. In this paper, a thorough discussion about the state of the art of mining data streams is presented followed by a formalization of our Algorithm Output Granularity (AOG) approach in mining data streams. The incorporation of AOG within a generic ubiquitous data mining system architecture is shown and discussed. The industrial applications of AOG-based mining techniques are given and discussed. © Springer-Verlag Berlin Heidelberg 2004.
History
Volume
3181Pagination
189 - 198Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783540229377ISBN-10
354022937XPublication classification
E1.1 Full written paper - refereedTitle of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC