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Timed-Causal State Splitting Reconstruction (T-CSSR) AlgorithmPowerPoint Presentation

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

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

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

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Sunday

Monday

Friday

Saturday

v1

p = 0.64

incoming

p = 0.23

v0

v2

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quiet

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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