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Predictive Mobility Models based on K th Markov Models

Frédéric Lassabe, Damien Charlet, Philippe Canalda, Pascal Chatonnay and François Spies firstname.lastname@pu-pm.univ-fcomte.fr LIFC Montbéliard ICPS2006 - 06/28/2006. Predictive Mobility Models based on K th Markov Models. Introduction. Where am I ?. What can I do ?. Where do I go ?.

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Predictive Mobility Models based on K th Markov Models

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  1. Frédéric Lassabe, Damien Charlet, Philippe Canalda, Pascal Chatonnay and François Spies firstname.lastname@pu-pm.univ-fcomte.fr LIFC Montbéliard ICPS2006 - 06/28/2006 Predictive Mobility Models based on Kth Markov Models

  2. ICPS 2006 - Lyon Introduction Where am I ? Whatcan I do ? Where do I go ? What will I do ?

  3. ICPS 2006 - Lyon Related Work Sarukkai 1st Order Markov Model (MM) Pitkow and Pirolli Longest Repeating Sequences Kth MM (KMM) merged into All-KMM (AKMM) Deshpande and Karypis “Light” AKMM Selective MM

  4. ICPS 2006 - Lyon K-to-1 past* AKMM shortcomings Error in iMM while jMM true (i>j) Apply to physical states Less connections → Compact data Use every KMM for prediction Threshold Determines if a state is selected Can trigger complete or partial handoff (resp. prefetch)

  5. ICPS 2006 - Lyon Experiments

  6. ICPS 2006 - Lyon Conclusions & Prospects Predictive power of Markov Models From web pages to mobility prediction Future works Analyse memory and computation costs Mobility Prediction and Multimedia Contents are real time, requires: Time in the model Fast algorithm (distributed ?) Comparison with others models Bayesian Inference Neural Networks

  7. Thank you for your attention Any questions ? ICPS 2006 - Lyon

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