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Manu Shukla. Spatiotemporal Pattern Mining Technique for Location-Based Service System Thi Hong Nhan, Jun Wook Lee and Keun Ho Ryu ETRI Journal, June 2008. Introduction. Authors propose techniques to discover frequent spatiotemporal patterns from moving objects data

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Manu shukla

Manu Shukla

Spatiotemporal Pattern Mining Technique for Location-Based Service SystemThi Hong Nhan, Jun Wook Lee and Keun Ho RyuETRI Journal, June 2008



  • 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



  • 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 oji, 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

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

Trajectory Generalization



Frequent 1 patterns

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-patterns

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

Predicting future locations

Predicting Future Locations



  • 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 results1

Experiment Results

Experiment results2

Experiment Results

Experiment results rlp

Experiment Results - RLP



  • 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

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