a location predictor based on dependencies between ...clu/lbsn2010/slides/nishino_lbsn2010.pdf ·...

A Location Predictor based on Dependencies Between Multiple Lifelog Data

Masaaki NishinoTakashi Yagi

Graduate School of Natural Science and Technology, Okayama University

Yukihiro Nakamura Shinyo Muto

NTT Cyber Solutions Laboratories, NTT Corporation

Masanobu Abe

1

ACM LBSN’10, November 2, 2010, San Jose, CA, USA

Copyright(c) 2010 NTT, All rights reserved.

Location Prediction

• Time limited sale infoat the supermarketsnear the predictedlocations

• Weather infoof future locations

•To-Do Tasks relatedto future locations

2

By predicting future locations, we can provide useful information to a user.

To-do tasksrelated to the

place

Time limitedSales


Related Works

Extract and use regularities in movement

from location log・Markov Model[Ashbrook, 2003]・Dynamic Bayesian Network[Liao, 2007]・Sequential Pattern Mining[Monreale, 2009]

accumulatedlocation logs

predictionmodels

3

Home

Station

Office

0.6

0.4

1.0

1.0


Problems

Since previous research uses regularities,

they cannot predict irregular movements.

Regulargo to school on weekdays

go to the gym every Monday

etc...

Irregularirregular meeting

business trips

etc...

4Copyright(c) 2010 NTT, All rights reserved.

Our proposal

Integrate different kinds of lifelogs to predict

both regular and irregular movements

Regulargo to school on weekdays

go to the gym every Monday.

etc...

Irregularirregular meeting

business trips

can predict with location log.

can predict with integrating different kinds of logs.


Calendar data

• People enter info about irregular events into it

• Widely used.

We use calendar data as a source for

making predictions of irregular movements


Dynamic Bayesian Networks modelfor integrating different data.

GPS Data

Calendar Data

DBN model can make reasonable predictions when prediction with only calendar/GPS data is difficult

7

Integrated DBN Model

Office OfficeBldg

A

“Lunch” “Bob”

Location

Stay Duration

Calendar

Time of Day Evening Evening Evening

10 min 0 min 15 min


Preprocesses

8

Stayed places

(Office)

(Station A)

ClusteringGPS Data Stayed Places

Calendar Data Extracting Keywords Keywords

(37.53,-122.08)(37.50,-122.00)

(Office)(Station A)

“Meeting with Bob”“See the Dentist”

“Meeting” , “Bob”“Dentist”

“Meeting” “Bob”

“Dentist”


Concepts of DBN Model

Place-place relationship

Home OfficeBuilding

A

Markov Model

Simple model for predicting locations.Can predict regular movements.

9

Location


Concepts of DBN Model

Place-Keyword relationship

Building A

“Meeting” “Bob”

・ Can predict irregular movements

Infer a user’s own relationshipbetween place and keywordfrom co-occurrences.

10

Location

Calendar


DBN model (basic)

Home OfficeBuilding

A


Integrate place-place relationship

and place-keyword relationship

11

Location

Calendar

Both regular and irregular movementscan be predicted.


DBN model (actual model)

Office OfficeBldg

A


Make some extensions to basic model.

- add node that represents time of day, stay duration

Location

Stay Duration

Calendar

Time of Day Evening Evening Evening

10 min 0 min 15 min


Learning

• Estimating the parameters of the probability distributions of DBN from data.

• Using maximum a posteriori (MAP) estimation.


Inference

14

Use the Viterbi algorithm to infer a state sequencethat maximizes probability.

InitialState

Calendar

Time of Day

10:00 10:10 10:20 10:30 10:40 10:50

Location

Stay Duration


Experimental Settings

Whether prediction accuracy is improved or not by

using calendar data.

Baseline:

DBN model without Calendar data

Dataset

GPS and calendar data of two subject

(about 50 days)


Evaluation metrics

• Evaluate the accuracy of prediction by changing the time difference between the subject of prediction and the time to start prediction

16

Time(ago)

Building A

Station A

Station B

12:00(0)

11:00(60)

10:00(120)

Subject ofthe prediction

Time to StartPrediction

Home


Prediction ResultsRegular Movements

There are no much differences.17

0.3

0.4

0.5

0.6

0.7

050100150200

GPSGPS+Calendar

Blue lineRed line

0.5

0.55

0.6

0.65

0.7

0.75

0.8

050100150200

Subject A Subject B


Prediction Results

18

0.1

0.2

0.3

0.4

0.5

0.6

050100150200

Irregular Movements

Subject A Subject B

The accuracy was improvedfor irregular movements

GPSGPS+Calendar

Blue lineRed line

0.1

0.15

0.2

0.25

0.3

0.35

050100150200


Example of Predictions

The prediction failed since the wrong place is estimated from the calendar entry


Example of Predictions

The result was modified because the time needed for movement was considered


Conclusion

• We show a DBN model for making prediction for both regular and irregular movements by using GPS and calendar data.

– The accuracy of predictions for irregular movementwas improved.

– Wrong prediction due to wrong schedule can be modified by using GPS data.

• Future works

– Use other kinds of logs.


Thank you.


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