Manu shukla
This presentation is the property of its rightful owner.
Sponsored Links
1 / 15

Manu Shukla PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

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

Download Presentation

Manu Shukla

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

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

  • Login