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Finding It Now: Construction and Configuration of Networked Classifiers in Real-Time Stream Mining Systems

  • Raphaël DucasseEmail author
  • Mihaela van der Schaar
Chapter

Abstract

As data is becoming more and more prolific and complex, the ability to process it and extract valuable information has become a critical requirement. However, performing such signal processing tasks requires to solve multiple challenges. Indeed, information must frequently be extracted (a) from many distinct data streams, (b) using limited resources, and (c) in real time to be of value. The aim of this chapter is to describe and optimize the specifications of signal processing systems, aimed at extracting in real time valuable information out of large-scale decentralized datasets. A first section will explain the motivations and stakes which have made stream mining a new and emerging field of research and describe key characteristics and challenges of stream mining applications. We then formalize an analytical framework which will be used to describe and optimize distributed stream mining knowledge extraction from large scale streams. In stream mining applications, classifiers are organized into a connected topology mapped onto a distributed infrastructure. We will study linear chains of classifiers and determine how the ordering of the classifiers in the chain impacts accuracy of classification and delay and determine how to choose the most suitable order of classifiers. Finally, we present a decentralized decision framework upon which distributed algorithms for joint topology construction and local classifier configuration can be constructed. Stream mining is an active field of research, at the crossing of various disciplines, including multimedia signal processing, distributed systems, machine learning etc. As such, we will indicate several areas for future research and development.

Keywords

False Alarm Operating Point Optimal Order Processing Node Misclassification Cost 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is based upon work supported by the National Science Foundation under Grant No. 1016081. We would like to thank Dr. Deepak Turaga (IBM Research) for introducing us to the topic of stream mining and for many productive conversation associated with the material of this chapter as well as providing us with Figs. 1 and 3 of this chapter. We also would like to thank Dr. Fangwen Fu and Dr. Brian Foo, who have been PhD students in Prof. van der Schaar group and have made contributions to the area of stream mining from which this chapter benefited. Finally, we thank Mr. Siming Song for kindly helping us with formatting and polishing the final version of the chapter.

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Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.The Boston Consulting GroupParisFrance
  2. 2.University of CaliforniaLos AngelesUSA

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