…. 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 ( firstname.lastname@example.org ) and Christopher H. Griffin ( email@example.com ).
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p = 0.64
p = 0.23
p = 0.13
Maria Vicente A. Bonto-Kane (firstname.lastname@example.org) and Christopher H. Griffin (email@example.com)
Cyberspace Sciences and Information Intelligence Research (CSIIR) Group, Computational Sciences Division, ORNL
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.
= stateTuple (currentState, timeInterval, dayOfWeek, timeOfDay)
2. Problem Scenario
Given time-series data like:
CSSR generated model
Timed-CSSR generated model
3. Application Domain: Mobile Devices
Smart Usability = Prediction + Automation
Examine usage statistics to track user events
4. Theoretical and Real World Issues
Use formal computational models to model user behavior
Develop smart software that adapts to users unique patterns of use