Towards Adjusting Mobile Devices To User's Behaviour

dc.contributor.authorFricke, Peter
dc.contributor.authorJungermann, Felix
dc.contributor.authorMorik, Katharina
dc.contributor.authorPiatkowski, Nico
dc.contributor.authorSpinczyk, Olaf
dc.contributor.authorStolpe, Marco
dc.contributor.authorStreicher, Jochen
dc.contributor.editorAtzmueller, Martin
dc.contributor.editorChin, Alvin
dc.contributor.editorHotho, Andreas
dc.contributor.editorStrohmaier, Markus
dc.date.accessioned2012-02-21T15:04:03Z
dc.date.available2012-02-21T15:04:03Z
dc.date.issued2012-02-21
dc.description.abstractMobile devices are a special class of resource-constrained em- bedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained re- sources 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, load- ing files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system’s operation. Our goal is to ad- just 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 ana- lyze 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.en
dc.identifier.isbn978-3642235986
dc.identifier.urihttp://hdl.handle.net/2003/29313
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-3304
dc.language.isoende
dc.relation.ispartofseriesAnalysis of Social Media and Ubiquitous Dataen
dc.subjectMining system callsen
dc.subjectubiquitous knowledge discoveryen
dc.subject.ddc004
dc.titleTowards Adjusting Mobile Devices To User's Behaviouren
dc.typeTextde
dc.type.publicationtypebookPartde
dcterms.accessRightsopen access

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