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Towards Adjusting Mobile Devices to User’s Behaviour

  • Peter Fricke
  • Felix Jungermann
  • Katharina Morik
  • Nico Piatkowski
  • Olaf Spinczyk
  • Marco Stolpe
  • Jochen Streicher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6904)

Abstract

Mobile devices are a special class of resource-constrained embedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained resources require extensive optimization of a mobile system compared to larger systems. Any needless operation has to be avoided. Time-consuming operations have to be started early on. For instance, loading files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system’s operation. Our goal is to adjust such strategies on the basis of logged system data. Optimization is then achieved by predicting an application’s behavior based on facts learned from earlier runs on the same system. In this paper, we analyze system-calls on operating system level and compare two paradigms, namely server-based and device-based learning. The results could be used to optimize the runtime behaviour of mobile devices.

Keywords

Mining system calls ubiquitous knowledge discovery 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Peter Fricke
    • 1
  • Felix Jungermann
    • 1
  • Katharina Morik
    • 1
  • Nico Piatkowski
    • 1
  • Olaf Spinczyk
    • 2
  • Marco Stolpe
    • 1
  • Jochen Streicher
    • 2
  1. 1.Artificial Intelligence GroupTU Dortmund UniversityDortmundGermany
  2. 2.Embedded System Software GroupTU Dortmund UniversityDortmundGermany

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