Where Next

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Roberto Trasarti

[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],? ? ? .4 .8 .35

[object Object],[object Object],[object Object],[object Object],Trajectory dataset Local patterns Prediction Tree

Select the set of interesting trajectories Validation Evaluation Extract T-Patterns (A set of Local models) Merge T-Patterns (Global model) Use the Condensed model as predictor

[object Object],F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi. Trajectory pattern mining . KDD 2007: 330-339.

[object Object],T-Pattern Rules α 1 α 2 α 3 R 1 R 2 R 3 R 4 R 1 R 2 R 3 R 4 R 1 R 2 R 3 R 4

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],How to compute the Best Match? Best Match Prediction

[object Object],Case a : The trajectory segment intersects the region of the node Case b : The enlarged trajectory segment intersects the region Case c : The enlarged trajectory segment doesn’t intersect the region Where the th_t is the time tolerance window defined by the user.

[object Object],[object Object],[object Object],[object Object],10 min 15 min 8 min 10 min Punctual score: 1 Punctual Score: .58 Punctual Score: .8 11 min 16 min Path score .79

[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],Model 1 Model 2 Testing the a priori evaluation

[object Object],[object Object],[object Object],[object Object],Predicted Location Cut Original Predicted Location Cut Original Error

[object Object],Training set : 4000 trajectories between 7am and 10 am on Wednesday Test set : 500 trajectories between 7am and 10 am on Thursday.

[object Object],Average Error vs th_space

[object Object],Single Users Accuracy and Prediction rate

[object Object],[object Object],Part of the GeoPKDD integrated platform. F. Giannotti, D. Pedreschi, and et al. Geopkdd: Geographic privacy-aware knowledge discovery and delivery (european project), 2008.

Trajectories Dataset Regions of Interest T-PATTERNS

[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Where Next

1. Anna Monreale Fabio Pinelli Roberto Trasarti Fosca Giannotti A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining . KDD 2009 Knowledge Discovery and Delivery Lab (ISTI-CNR & Univ. Pisa) ‏ www-kdd.isti.cnr.it

7. Select the set of interesting trajectories Validation Evaluation Extract T-Patterns (A set of Local models) Merge T-Patterns (Global model) Use the Condensed model as predictor

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