Spatiotemporal Pattern Mining Technique for Location-Based Service System

Thi Hong Nhan, Jun Wook Lee and Keun Ho RyuETRI Journal, June 2008

Manu Shukla

Introduction

Authors propose techniques to discover frequent spatiotemporal patterns from moving objects data

Patterns found can help service provider send information to a user in a push driven manner and predict future location of user

Includes two algorithms AIIMOP and MaxMOP to find frequent and maximal patterns respectively

To control the density of pattern regions and automatically adjust the shape and size of regions, employ grid based clustering technique

Definitions

• Trajectory: finite sequence of points {(oj,p1,vt1),(oj,p2,vt2),….,(oj,pn,vtn)} in the XxYxT space where pi is represented by coordinates xi,yi at the sampled time vti for 1<=i<=n

• Moving sequence; list of temporally ordered region labels ms=<(a1,t1,(a2,t2),…(aq,tq)> where ai contains oj

i, ti-ti+1 >>τ and tq-t1 <=max_span.end – max_span.start for q<=T and 1<=i<=q

• Subsequence

• Frequent Patterns: If ms has support(ms) >= min_sub where min_sub is user-specified, then ms is defined as frequent pattern.

Pattern Movements

• Provided function MINE_MOP to allow the adoption of the type of patterns authors wish to obtain with same input

• Trajectory reconstructions: results of re-sampling trajectories

Trajectory Generalization

AIIMOP

Frequent 1-patterns

• Decompose a dataset of moving objects into groups of moving points, each Ai={oji|oji ͼ ai} for one timestamp vti

• Frequenty 1-patterns are dense regions or clusters discovered from Ai

Frequent k-patterns

• Frequent k-pattern is created by merging a pair of frequent 1-patterns in the consideration of the time constraint.

Predicting Future Locations

Experiments

• Validated efficiency of AIIMOP and MaxMOP under diverse parameters and datasets and by comparing them with grid-based technique using the GSP and DFS_MINE algorithm

• Used Synthetic dataset

Experiment Results

Experiment Results

Experiment Results

Experiment Results - RLP

Conclusions

• The patterns mined in algorithms presented can be used to target users

• Can be used to make the location-based services more efficient and effective