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Transcript of Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying 1, Wang-Chien Lee 2, Tz-Chiao Weng...
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
Intelligent DataBase System Lab, NCKU, Taiwan
Outline
2
Introduction
Location Prediction
Data Preprocessing
Semantic Mining
Geographic Mining
Matching Strategy & Scoring Function
Experiments
Conclusions
Intelligent DataBase System Lab, NCKU, Taiwan
Application Background
3
?
??
?
Location based services
navigational services
traffic management
location-based advertisement
Predict next location
Effective marketing
Efficient system operation
Intelligent DataBase System Lab, NCKU, Taiwan
Research Motivation
4
Frequent Pattern based Prediction Model
Frequent movement behavior of users
Geographic features of user trajectories
geographic properties
Distance
Shape
Velocity
…
Intelligent DataBase System Lab, NCKU, Taiwan
An example
5
Trajectory1Trajectory2
Trajectory3Geographic Point
Trajectory1Trajectory2
Trajectory3Geographic Point
Intelligent DataBase System Lab, NCKU, Taiwan
An example
6
School
Park
Hospital
Bank
Restaurant
Trajectory1Trajectory2
Trajectory3Geographic Point
School
Park
Hospital
Bank
Restaurant
Trajectory1Trajectory2
Trajectory3Geographic Point
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
Outline
8
Introduction
SemanPredict framework
Data Preprocessing
Semantic Mining
Geographic Mining
Matching Strategy & Scoring Function
Experiments
Conclusions
Intelligent DataBase System Lab, NCKU, Taiwan
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
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
Intelligent DataBase System Lab, NCKU, Taiwan
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.
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
Data Preprocessing
Intelligent Database Laboratory, CSIE, NCKU - 12 -12
Stay Location6
Stay Location5 Trajectory2
Trajectory3
Stay Location2
Stay Location1
Stay Location4
Stay Location3
Trajectory1
Intelligent DataBase System Lab, NCKU, Taiwan
Framework
13
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
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
Intelligent DataBase System Lab, NCKU, Taiwan14
Stay Location Sequence Set
of User 1…
Semantic Trajectory Pattern Mining
User 1’s Semantic Trajectory Pattern
Tree
User 2’s Semantic Trajectory Pattern
Tree
User 3’s Semantic Trajectory Pattern
Tree
User k’s Semantic Trajectory Pattern
Tree
User Clustering based on MSTP-similarityUser
Clusters
…
Stay Location Sequence Set
of User 2
Stay Location Sequence Set
of User 3
Stay Location Sequence Set
of User k
Geographic semantic
information database
Stay Location Sequence Set
of User 1…
Semantic Trajectory Pattern Mining
User 1’s Semantic Trajectory Pattern
Tree
User 2’s Semantic Trajectory Pattern
Tree
User 3’s Semantic Trajectory Pattern
Tree
User k’s Semantic Trajectory Pattern
Tree
User Clustering based on MSTP-similarityUser
Clusters
…
Stay Location Sequence Set
of User 2
Stay Location Sequence Set
of User 3
Stay Location Sequence Set
of User k
Geographic semantic
information database
Mining User Similarity from Semantic Trajectories. In Proceedings of LBSN' 10.
Intelligent DataBase System Lab, NCKU, Taiwan
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>
Intelligent DataBase System Lab, NCKU, Taiwan
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>
Intelligent DataBase System Lab, NCKU, Taiwan
Framework
17
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
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
Intelligent DataBase System Lab, NCKU, Taiwan18
Cluster 1’s Stay Location Pattern
Tree
Cluster 2’s Stay Location Pattern
Tree
Cluster 3’s Stay Location Pattern
Tree
Cluster h’s Stay Location Pattern
Tree
…
Trajectory of Cluster 1
Trajectory of Cluster 2
Trajectory of Cluster 3
Trajectory of Cluster h
…
User Clusters
Users’ Stay Location Sequences Grouping Based on Clustering Result
Stay Location Pattern Mining
…
Stay Location Sequence Set
of User 1…
Stay Location Sequence Set
of User 2
Stay Location Sequence Set
of User 3
Stay Location Sequence Set
of User k
Cluster 1’s Stay Location Pattern
Tree
Cluster 2’s Stay Location Pattern
Tree
Cluster 3’s Stay Location Pattern
Tree
Cluster h’s Stay Location Pattern
Tree
…
Trajectory of Cluster 1
Trajectory of Cluster 2
Trajectory of Cluster 3
Trajectory of Cluster h
…
User Clusters
Users’ Stay Location Sequences Grouping Based on Clustering Result
Stay Location Pattern Mining
…
Stay Location Sequence Set
of User 1…
Stay Location Sequence Set
of User 2
Stay Location Sequence Set
of User 3
Stay Location Sequence Set
of User k
Intelligent DataBase System Lab, NCKU, Taiwan
Framework
19
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
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
Intelligent DataBase System Lab, NCKU, Taiwan
Matching Strategy & Scoring Function
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
Intelligent DataBase System Lab, NCKU, Taiwan
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)
(Stay Location3, 0.667)
(Stay Location0, 0.7)
( , )
(Stay Location1, 1.0)
(Stay Location3, 0.667)(Stay Location3, 0.667)(Stay Location1, 0.667)
(Stay Location3, 0.667)
Candidate paths GeographicScore
(Stay Location3) (Stay Location0)
(Stay Location1)0
Intelligent DataBase System Lab, NCKU, Taiwan
Geographic Score and Candidate Paths
22
(Stay Location0
( , )
(Stay Location1, 1.0)
(Stay Location3, 0.667)(Stay Location3, 0.667)(Stay Location1, 0.667)
(Stay Location3, 0.667)
(Stay Location0, 0. 7)
( , )
(Stay Location1, 1.0)
(Stay Location3, 0.667)(Stay Location3, 0.667)(Stay Location1, 0.667)
(Stay Location3, 0.667)
Candidate paths GeographicScore
(Stay Location0) (Stay Location1) 0.8 × 0.7 + 0.667 = 1.2
User current movement: < , Stay Location >User current movement: < Stay Location0 , Stay Location1>
Intelligent DataBase System Lab, NCKU, Taiwan
Geographic Score and Candidate Paths
23
(Stay Location0
( , )
(Stay Location1, 1.0)
(Stay Location3, 0.667)(Stay Location3, 0.667)(Stay Location1, 0.667)
(Stay Location3, 0.667)
(Stay Location0, 0.7)
( , )
(Stay Location1, 1.0)
(Stay Location3, 0.667)(Stay Location3, 0.667)(Stay Location1, 0.667)
(Stay Location3, 0.667)
Candidate paths GeographicScore
(Stay Location1) 1.0
User current movement: < >User current movement: < Stay Location1>
Intelligent DataBase System Lab, NCKU, Taiwan
Candidate Paths Transformation
24
α=0.8
Candidate paths GeographicScore
(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)
Intelligent DataBase System Lab, NCKU, Taiwan
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 , 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)(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 Seq. SemanticScore
(Unknown) (School) 0.8 × 0.667 + 0.667 = 1.2
(School) 1.0
Intelligent DataBase System Lab, NCKU, Taiwan
Outline
26
Introduction
Location Prediction
Semantic Mining
Geographic Mining
Matching Strategy & Scoring Function
Experiments
Conclusions
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
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%
Intelligent DataBase System Lab, NCKU, Taiwan
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
none-ClusteringOur approach
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
none-ClusteringOur approach
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
Outline
32
Introduction
Location Prediction
Semantic Mining
Geographic Mining
Matching Strategy & Scoring Function
Experiments
Conclusions
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
Thank you for your attention
Quetion?
34
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
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
Intelligent DataBase System Lab, NCKU, Taiwan
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
t}{Restauran Bank}, {Park,{Cinema}, {School}, P
8
5
411
21
11
)(
Pratio
Intelligent DataBase System Lab, NCKU, Taiwan
MSTP-Similarity
Similarity of two patterns
||||
)(||)(||),(-
t}{Restauran {Park}, Market}, {School, Q
t}{Restauran Bank}, {Park,{Cinema}, {School}, P
QP
QratioQPratioPQPSimilarityMSTP