Timed causal state splitting reconstruction t cssr algorithm
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…. 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 ( [email protected] ) and Christopher H. Griffin ( [email protected] ).

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

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Timed causal state splitting reconstruction t cssr algorithm

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 ([email protected]) and Christopher H. Griffin ([email protected])

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


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