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A Data Mining Approach for Location Prediction in Mobile Environments

Data & Knowledge Engineering

Volume 54, Issue 2, August 2005, Pages 121–146

劉康全

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Introduction

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Motivation

• Predicted movement can be used for effectively allocating resources instead of blindly allocating excessive resources

• Benefit to the broadcast program generation, data items can be broadcast to the predicted cell

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Definition

UAP ID UAP

1 <5, 6, 0, 4, 5>

2 <3, 4, 5, 0>

3 <1, 2, 3, 4, 0, 5>

4 <3, 2, 0>

Database of UAPs An example coverage region and corresponding graph G

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Definition

• example: UAP A=<3, 4, 0, 1, 5> and pattern B=<4, 5>

A = 3 4 0 1 5 B = 4 - - 5

suppInc

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Definition

• confidence(X→Y)

• example:

R:<2> <0>, <2> = 2 , <2,0> = 1.33

confidence(R)

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Three phases for the algorithm

1. Mining UMPs from Graph Traversals: Find mobility

patterns

2. Generation of Mobility Rules: Find Mobility rules

from mobility patterns

3. Mobility Prediction: Prediction of next inter-cell

movement based on mobility rules

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UMP mining

CAND SUPP

<0> 4

<1> 1

<2> 2

<3> 3

<4> 3

<5> 3

<6> 1

<7> 0

<8> 0

Pattern SUPP

<0> 4

<2> 2

<3> 3

<4> 3

<5> 3

UAP ID UAP

1 <5, 6, 0, 4, 5>

2 <3, 4, 5, 0>

3 <1, 2, 3, 4, 0, 5>

4 <3, 2, 0>

Database of UAPs

corresponding graph G

minsup=1.33

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UMP mining

Pattern SUPP

<0> 4

<2> 2

<3> 3

<4> 3

<5> 3

CAND SUPP CAND SUPP

<0,1> 0 <3,2> 1

<0,2> 0 <3,4> 2

<0,3> 0 <4,0> 1.5

<0,4> 1 <4,3> 0

<0,5> 1.5 <4,5> 2.5

<0,6> 0 <5,0> 0

<2,0> 1.33 <5,4> 1.5

<2,1> 0 <5,6> 0.33

<2,3> 1 <5,8> 1

<3,0> 1.33

UAP ID UAP

1 <5, 6, 0, 4, 5>

2 <3, 4, 5, 0>

3 <1, 2, 3, 4, 0, 5>

4 <3, 2, 0>

Database of UAPs

corresponding graph G

minsup=1.33

supp<0,5>

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UMP mining

Large

Pattern SUPP Pattern SUPP

<0> 4 <3,0> 1.33

<2> 2 <3,4> 2

<3> 3 <4,0> 1.5

<4> 3 <4,5> 2.5

<5> 3 <5,0> 1.5

<0,5> 1.5 <3,4,0> 1.5

<0,2> 1.33 <3,4,5> 1.5

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Three phases for the algorithm

1. Mining UMPs from Graph Traversals: Movement data mined

for discovering regularities (UMP) in inter-cell movements?

2. Generation of Mobility Rules: Mobility rules are extracted

from UMPs?

3. Mobility Prediction: Prediction of next inter-cell movement

based on mobility rules

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Mobility Rules

Large Pattern SUPP Pattern SUPP

<0> 4 <3,0> 1.33

<2> 2 <3,4> 2

<3> 3 <4,0> 1.5

<4> 3 <4,5> 2.5

<5> 3 <5,0> 1.5

<0,5> 1.5 <3,4,0> 1.5

<2,0> 1.33 <3,4,5> 1.5

Mobility RulesRule Conf

<0>→<5> 35

<2>→<0> 66.6

<3>→<0> 44.2

<3>→<4> 66.6

<4>→<0> 50

<4>→<5> 83.33

<5>→<0> 50

<3>→<4,0> 50

<3,4>→<0> 75

<3>→<4,5> 50

<3,4>→<5> 75R : <2>→<0> Conf :

minconf=50%

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Three phases for the algorithm

1. Mining UMPs from Graph Traversals: Movement data mined

for discovering regularities (UMP) in inter-cell movements?

2. Generation of Mobility Rules: Mobility rules are extracted

from UMPs?

3. Mobility Prediction: Prediction of next inter-cell movement

based on mobility rules

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Mobility PredictionExample: Assume that the current trajectory of the user is P=<2, 3, 0, 4> Matching Rules: (support+confidence) <4> → <0> 1.5+50=51.5<4> → <5> 2.5+83.33=85.83<3, 4> → <0> 1.5+75=76.5<3, 4> → <5> 1.5+75=76.5Sorted tuple array is: TupleArray = [(5, 85.83), (0, 76.5)] If m=1, then Predicted Cells Set = {5}If m=2, then Predicted Cells Set = {5, 0}

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Conclusion

• A data mining algorithm for the prediction of user movements in a mobile computing system

• Algorithm is based on– Mining the mobility patterns of users– Then forming mobility rules from these patterns– Finally predicting a mobile user’s next movements by using the mobility

rules

• A good performance when compared to the performance of Ignorant Method