Foundations of adaptive data stream mining for mobile and embedded applications

M. Gaber

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Mining data streams for mobile and embedded applications faces a major problem represented in the high rate of the streaming input with regard to the available computational resources. Adapting the data mining algorithms to the availability of resources is an essential step towards realizing the potential applications in this area. In this paper, we review our Algorithm Output Granularity (AOG) for data stream mining adaptation. The generalization of AOG based on Probably Approximately Correct (PAC) learning model is presented. This generalization is of paramount importance to establish a theoretical framework for adaptation and resource-awareness in data stream mining.
Original languageEnglish
Title of host publicationBiomedical Engineering Conference, 2008. CIBEC 2008. Cairo International
Place of PublicationPiscataway
PublisherIEEE
Pages1 -6
Number of pages6
ISBN (Print)9781424426942
Publication statusPublished - Dec 2008
EventBiomedical Engineering Conference - Cairo, Egypt
Duration: 18 Dec 200820 Dec 2008

Conference

ConferenceBiomedical Engineering Conference
Abbreviated titleCIBEC 2008
Country/TerritoryEgypt
CityCairo
Period18/12/0820/12/08

Keywords

  • adaptation
  • adaptive data stream mining
  • algorithm output granularity
  • embedded application
  • mobile application
  • probably approximately correct learning model
  • resource awareness
  • adaptive systems
  • data mining
  • embedded systems
  • medical information systems
  • mobile computing

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