semantic trajectory mining for location prediction

Intelligent DataBase System Lab, NCKU, Taiwan

Josh Jia-Ching Ying1, Wang-Chien Lee2, Tz-Chiao Weng1 and Vincent S. Tseng1

1 Department of Computer Science & Information Engineering, National Cheng Kung University, Taiwan, ROC

2 Department of Computer Science & Engineering Pennsylvania State University, PA 16802, USA

Semantic Trajectory Mining for Location Prediction


Outline

2

Introduction

Location Prediction

Data Preprocessing

Semantic Mining

Geographic Mining

Matching Strategy & Scoring Function

Experiments

Conclusions


Application Background

3

?

??

?

Location based services

navigational services

traffic management

location-based advertisement

Predict next location

Effective marketing

Efficient system operation


Research Motivation

4

Frequent Pattern based Prediction Model

Frequent movement behavior of users

Geographic features of user trajectories

geographic properties

Distance

Shape

Velocity

…


An example

5

Trajectory1Trajectory2

Trajectory3Geographic Point




An example

6

School

Park

Hospital

Bank

Restaurant



School

Park

Hospital

Bank

Restaurant




Semantic trajectory Pattern

7

Frequent Pattern based Prediction Model

Frequent behaviors of users

Frequent movement behavior

Geographic features of user trajectories

Semantic trajectory

Frequent semantic behavior


Outline

8

Introduction

SemanPredict framework

Data Preprocessing

Semantic Mining

Geographic Mining


Experiments

Conclusions


Framework

9

Geographic semantic

information database

Trajectory logs

Semantic Mining

Geographic Mining

User Clusters

Offline Training Module

Online Prediction Module

Individual Semantic Trajectory

Pattern Trees

Cluster Stay Location Pattern Trees

Stay location sequence

Geographic Score

Calculation

Semantic Score

Calculation

Possible Next Location

Candidate Paths

Stay Location Sequences

Data Preprocessing

Current Trajectory

Stay Location Sequence

Transformation

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation


Data Preprocessing

10

To transforms each user’s GPS trajectories into stay location sequences.

The stay location is a location where users stops for a while.

Most activities of a mobile user are usually performed at where the user stays.


Data Preprocessing

Intelligent Database Laboratory, CSIE, NCKU - 11 -

Stay Location1

Stay Location3

Stay Location2

11

Recommending Friends and Locations Based on Individual Location History Y. Zheng, L. Zheng, Z. Ma, X. Xie, W. Y. MaVLDB Journal 2010

Trajectory1

Trajectory2

Trajectory3

Stay Point


Data Preprocessing

Intelligent Database Laboratory, CSIE, NCKU - 12 -12

Stay Location6

Stay Location5 Trajectory2

Trajectory3

Stay Location2

Stay Location1

Stay Location4

Stay Location3

Trajectory1


Framework

13

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation

Intelligent DataBase System Lab, NCKU, Taiwan14

Stay Location Sequence Set

of User 1…

Semantic Trajectory Pattern Mining

User 1’s Semantic Trajectory Pattern

Tree


Tree


Tree

User k’s Semantic Trajectory Pattern

Tree

User Clustering based on MSTP-similarityUser

Clusters

…


of User 2


of User 3


of User k

Geographic semantic



of User 1…

Semantic Trajectory Pattern Mining


Tree


Tree


Tree

User k’s Semantic Trajectory Pattern

Tree

User Clustering based on MSTP-similarityUser

Clusters

…


of User 2


of User 3


of User k

Geographic semantic


Mining User Similarity from Semantic Trajectories. In Proceedings of LBSN' 10.


Semantic Trajectory Pattern

15

Minimum support = 60%

Support(<School, Park>) = 2/3 > 60%

<School, Park> is a semantic trajectory pattern

Trajectory Semantic trajectory

Trajectory1 < School, Bank, Restaurant >

Trajectory2 < Unknown, School, Park, Hospital>

Trajectory3 <Unknown, School, Park, Hospital>


Semantic Trajectory Pattern Tree

16

Pattern Support

A 4

B 6

C 3

D 5

E 3

AB 3

BC 3

BD 3

DE 3

ABC 3

root

<A,4> <B,6> <C,3> <D,5>

<B,3> <C,3> <D,3> <E,3>

<C,3>

<E,3>


Framework

17

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation

Intelligent DataBase System Lab, NCKU, Taiwan18

Cluster 1’s Stay Location Pattern

Tree


Tree


Tree

Cluster h’s Stay Location Pattern

Tree

…

Trajectory of Cluster 1



Trajectory of Cluster h

…

User Clusters

Users’ Stay Location Sequences Grouping Based on Clustering Result

Stay Location Pattern Mining

…


of User 1…


of User 2


of User 3


of User k


Tree


Tree


Tree

Cluster h’s Stay Location Pattern

Tree

…




Trajectory of Cluster h

…

User Clusters

Users’ Stay Location Sequences Grouping Based on Clustering Result

Stay Location Pattern Mining

…


of User 1…


of User 2


of User 3


of User k


Framework

19

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation

Geographic semantic


Trajectory logs

Semantic Mining

Geographic Mining

User Clusters




Pattern Trees



Geographic Score

Calculation

Semantic Score

Calculation


Candidate Paths


Data Preprocessing

Current Trajectory


Transformation



Scoring Function

Matching Strategy outdated moves may potentially deteriorate the precision of

predictions.

more recent moves potentially have more important impacts on predictions .

the matching path with a higher support and a higher length may provide a greater confidence for predictions.

20

10

,)1(

where

oreSemanticScScoreGeographicScore


Geographic Score and Candidate Paths

21

User current movement: <Stay Location , Stay Location , Stay Location >User current movement: <Stay Location3 , Stay Location0 , Stay Location1>

(Stay Location0

( , )

(Stay Location1, 1.0)

(Stay Location3, 0.667)(Stay Location3, 0.667)(Stay Location1, 0.667)



( , )




Candidate paths GeographicScore

(Stay Location3) (Stay Location0)

(Stay Location1)0



22

(Stay Location0

( , )




(Stay Location0, 0. 7)

( , )





(Stay Location0) (Stay Location1) 0.8 × 0.7 + 0.667 = 1.2

User current movement: < , Stay Location >User current movement: < Stay Location0 , Stay Location1>



23

(Stay Location0

( , )





( , )





(Stay Location1) 1.0

User current movement: < >User current movement: < Stay Location1>


Candidate Paths Transformation

24

α=0.8


(Stay Location0) (Stay Location1) 0.8 × 0.7 + 0.667 = 1.2

(Stay Location1) 1.0

Candidate Paths Semantic Candidate Paths

(Stay Location0) (Stay Location1) (Unknown) (School)

(Stay Location1) (School)


Semantic Score

25

Semantic candidate path: (Unknown) (School)

k = 1 k = 2

k = 1 k = 2

(School, 1.0)

( , )

(Park, 1.0)

(Hospital , 0.667)

(Hospital , 0.667)

(Hospital , 0.667)(Park , 1.0)

(Hospital , 0.667)

(Unknown, 0.667) (School, 1.0) ({Park, Bank}, 1.0)

({Park, Bank}, 1.0)

(Hospital , 0.667)

(Hospital , 0.667) (Hospital , 0.667)

Semantic candidate path: (Unknown) (School)

k = 1 k = 2

k = 1 k = 2

(School, 1.0)

( , )

(Park, 1.0)

(Hospital , 0.667)

(Hospital , 0.667)


(Hospital , 0.667)


({Park, Bank}, 1.0)

(Hospital , 0.667)

(Hospital , 0.667) (Hospital , 0.667)(School, 1.0)

( , )

(Park, 1.0)

(Hospital , 0.667)

(Hospital , 0.667)


(Hospital , 0.667)


({Park, Bank}, 1.0)

(Hospital , 0.667)

(Hospital , 0.667) (Hospital , 0.667)

Semantic Candidate Seq. SemanticScore

(Unknown) (School) 0.8 × 0.667 + 0.667 = 1.2

(School) 1.0


Outline

26

Introduction

Location Prediction

Semantic Mining

Geographic Mining


Experiments

Conclusions


Experiments

27

MIT reality mining datasetThe Reality Mining project was conducted from 2004-2005

at the MIT Media Laboratory106 mobile users14391 Trajectories

Cell span

Cell name


Experiments

28

Sensitivity Tests

0.6

0.65

0.7

0.75

1 0.8 0.6 0.4 0.2

α

Prec

isio

n

minimum support = 1%minimum support = 3%

0.6

0.65

0.7

0.75

1 0.8 0.6 0.4 0.2

βPr

ecis

ion

minimum support = 1%minimum support = 3%


Experiments

29

Impact of the semantic clustering

0.4

0.5

0.6

0.7

0.8

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support(%)

F-m

easu

re

none-ClusteringOur approach

0.4

0.5

0.6

0.7

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support(%)

Prec

esio

n


0.4

0.5

0.6

0.7

0.8

0.9

1

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support(%)R

ecal

l



Experiments

30

Comparison of Prediction Strategies

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support(%)

F-m

easu

reGO SemanPredict FM

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support (%)

Pre

cesi

on

GO SemanPredict FM

0.10.20.30.40.50.60.70.80.9

1

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support (%)

Rec

all

GO SemanPredict FM

Geographic Only: GO

Full-Matching: FM


Experiments

31

Efficiency Evaluation

0

5

10

15

20

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support (%)

Ex

ecu

tio

n t

ime

(Sec

.)

SemanPredict( and FM) GO

0

0.5

1

1.5

2

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Minimum support (%)E

xec

uti

on

tim

e(m

s.)

GO SemanPredict FM


Outline

32

Introduction

Location Prediction

Semantic Mining

Geographic Mining


Experiments

Conclusions


Conclusions

33

A novel framework to predict the next location of a mobile user in support of various location-based services

both semantic and geographic information

A novel cluster-based prediction technique to predict the next location of a mobile user


Thank you for your attention

Quetion?

34


MSTP-Similarity

35

Similarity of two users:

P1

…Pm

P1’…Pn’

There are m×n MSTP-Similarity

user Uuser V

)',(

)',(-)',(

),(

1 1

1 1

m

i

n

jji

m

i

n

jjiji

PPweight

PPSimilariyMSTPPPweight

VUSimilarity


Semantic Trajectory Pattern

36

Semantic trajectory

Geographic semantic information database

a customized spatial database which stores the semantic information of landmarks that we collect via Google Map

<Stay Location3, Stay Location1, Location2>

<Restaurant, Park , School>

Frequent Pattern

Prefix-Span


MSTP-Similarity

37

the ratio of common part

t}{Restauran {Park}, {School}, Q)LCS(P,

t}{Restauran {Park}, Market}, {School, Q

t}{Restauran Bank}, {Park,{Cinema}, {School}, P

2

1

1

1 1

1


8

5

411

21

11

)(

Pratio


MSTP-Similarity

Similarity of two patterns

||||

)(||)(||),(-

t}{Restauran {Park}, Market}, {School, Q


QP

QratioQPratioPQPSimilarityMSTP

semantic trajectory mining for location prediction

Documents