1 / 1

Timed-Causal State Splitting Reconstruction (T-CSSR) Algorithm

…. Sunday. Monday. Friday. Saturday. v 1. p = 0.64. incoming. p = 0.23. v 0. v 2. quiet. p=1.0. p = 0.13. A. B. v 3. p=0.3. p=0.7. voiceMail. by. Maria Vicente A. Bonto-Kane ( bontokanema@ornl.gov ) and Christopher H. Griffin ( griffinch@ornl.gov ).

avak
Download Presentation

Timed-Causal State Splitting Reconstruction (T-CSSR) Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. … Sunday Monday Friday Saturday v1 p = 0.64 incoming p = 0.23 v0 v2 . . . . . . quiet . . . . . . p=1.0 p = 0.13 A B v3 p=0.3 p=0.7 voiceMail by Maria Vicente A. Bonto-Kane (bontokanema@ornl.gov) and Christopher H. Griffin (griffinch@ornl.gov) Cyberspace Sciences and Information Intelligence Research (CSIIR) Group, Computational Sciences Division, ORNL Timed-Causal State Splitting Reconstruction (T-CSSR) Algorithm 1. Problem Statement Timed-CSSR Algorithm (generates formal probabilistic model) Current methods for pattern analysis of time-series data rely on pattern matching (fitting Hidden Markov models to the data). Shalizi et al. designed an algorithm for recognizing unknown patterns and discovering Markov models present in time-series data. This research extends Shalizi’s method to discovering not just event patterns but also durations in the event patterns. Analysis of cellphone data shows usefulness of this algorithm. vs = stateTuple (currentState, timeInterval, dayOfWeek, timeOfDay) 2. Problem Scenario Given time-series data like: ABBA..B…ABBA..B..ABBA...B…ABBA..B..ABBA…B…ABBA CSSR generated model Timed-CSSR generated model outgoing A B B A B 3. Application Domain: Mobile Devices Probabilistic Models Usage Statistics • Mobile devices that can: • Track events that happen with regularity • Predict activities of a user • Anticipate the wanted operation • Deliver the needed function or operation • Derive statistics on types/frequency of operations as they relate to: • Time of day • Day of the week • Bayesian probability • Discrete/Continuous Time Markov Model • Poisson Model • Markov-Modulated Poisson Model Smart Usability = Prediction + Automation Examine usage statistics to track user events 4. Theoretical and Real World Issues • Discover what computational models help develop smart functionality? • What types of events can be automated? routine? occasional but critical events? • Is automation feasible, desirable to users? • Will automation promote regular habits? • Will people with regular habits benefit more from automation? Use formal computational models to model user behavior Develop smart software that adapts to users unique patterns of use http://www.marivicbontokane.com/research/ornl2008Poster-mabk&griffin.pdf

More Related