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A novel framework for design and implementation of adaptive stream mining systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
Publication statusPublished - 2013
Publication typeA4 Article in a conference publication
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: 15 Jul 201319 Jul 2013

Conference

Conference2013 IEEE International Conference on Multimedia and Expo, ICME 2013
CountryUnited States
CitySan Jose, CA
Period15/07/1319/07/13

Abstract

With the increasing need for accurate mining and classification from multimedia data content, and the growth of such multimedia applications in mobile and distributed architectures, stream mining systems require increasing amounts of flexibility, extensibility, and adaptivity for effective deployment. To address this challenge, we propose a novel approach that rigorously integrates foundations of dataflow modeling for high level signal processing system design, and adaptive stream mining based on dynamic topologies of classifiers. In particular, we introduce a new design environment, called the lightweight dataflow for dynamic data driven application systems (LiD4E) environment. LiD4E provides formal semantics, rooted in dataflow principles, for design and implementation of a broad class of multimedia stream mining topologies. We demonstrate the capabilities of LiD4E using a face detection application that systematically adapts the type of classifier used based on dynamically changing application constraints.

Keywords

  • Adaptive stream mining, dataflow graphs, dynamic data-driven adaptive systems