Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying 1, Wang-Chien Lee 2, Tz-Chiao Weng 1 and Vincent S. Tseng 1 1 Department of Computer.

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Presentation transcript:

Intelligent DataBase System Lab, NCKU, Taiwan Josh Jia-Ching Ying 1, Wang-Chien Lee 2, Tz-Chiao Weng 1 and Vincent S. Tseng 1 1 Department of Computer Science & Information Engineering, National Cheng Kung University, Taiwan, ROC 2 Department of Computer Science & Engineering Pennsylvania State University, PA 16802, USA Semantic Trajectory Mining for Location Prediction

Intelligent DataBase System Lab, NCKU, Taiwan Outline 2 Introduction Location Prediction Data Preprocessing Semantic Mining Geographic Mining Matching Strategy & Scoring Function Experiments Conclusions

Intelligent DataBase System Lab, NCKU, Taiwan Application Background 3 ? ? ? ? Location based services navigational services traffic management location-based advertisement Predict next location Effective marketing Efficient system operation

Intelligent DataBase System Lab, NCKU, Taiwan Research Motivation 4 Frequent Pattern based Prediction Model Frequent movement behavior of users Geographic features of user trajectories geographic properties Distance Shape Velocity …

Intelligent DataBase System Lab, NCKU, Taiwan An example 5 Trajectory Geographic Point Trajectory Geographic Point

Intelligent DataBase System Lab, NCKU, Taiwan An example 6

Intelligent DataBase System Lab, NCKU, Taiwan Semantic trajectory Pattern 7 Frequent Pattern based Prediction Model Frequent behaviors of users Frequent movement behavior Geographic features of user trajectories Semantic trajectory Frequent semantic behavior

Intelligent DataBase System Lab, NCKU, Taiwan Outline 8 Introduction SemanPredict framework Data Preprocessing Semantic Mining Geographic Mining Matching Strategy & Scoring Function Experiments Conclusions

Intelligent DataBase System Lab, NCKU, Taiwan Framework 9

Intelligent DataBase System Lab, NCKU, Taiwan Data Preprocessing 10 To transforms each user’s GPS trajectories into stay location sequences. The stay location is a location where users stops for a while. Most activities of a mobile user are usually performed at where the user stays.

Intelligent DataBase System Lab, NCKU, Taiwan Data Preprocessing Intelligent Database Laboratory, CSIE, NCKU Stay Location 1 Stay Location 3 Stay Location 2 11 Recommending Friends and Locations Based on Individual Location History Y. Zheng, L. Zheng, Z. Ma, X. Xie, W. Y. Ma VLDB Journal 2010 Trajectory 1 Trajectory 2 Trajectory 3 Stay Point

Intelligent DataBase System Lab, NCKU, Taiwan Data Preprocessing Intelligent Database Laboratory, CSIE, NCKU Stay Location 6 Stay Location 5 Trajectory 2 Trajectory 3 Stay Location 2 Stay Location 1 Stay Location 4 Stay Location 3 Trajectory 1

Intelligent DataBase System Lab, NCKU, Taiwan Framework 13

Intelligent DataBase System Lab, NCKU, Taiwan 14 Mining User Similarity from Semantic Trajectories. In Proceedings of LBSN' 10.

Intelligent DataBase System Lab, NCKU, Taiwan Semantic Trajectory Pattern 15 Minimum support = 60% Support( ) = 2/3 > 60% is a semantic trajectory pattern TrajectorySemantic trajectory Trajectory 1 Trajectory 2 Trajectory 3

Intelligent DataBase System Lab, NCKU, Taiwan Semantic Trajectory Pattern Tree 16 PatternSupport A4 B6 C3 D5 E3 AB3 BC3 BD3 DE3 ABC3 root

Intelligent DataBase System Lab, NCKU, Taiwan Framework 17

Intelligent DataBase System Lab, NCKU, Taiwan 18

Intelligent DataBase System Lab, NCKU, Taiwan Framework 19

Intelligent DataBase System Lab, NCKU, Taiwan Matching Strategy & Scoring Function Scoring Function Matching Strategy outdated moves may potentially deteriorate the precision of predictions. more recent moves potentially have more important impacts on predictions. the matching path with a higher support and a higher length may provide a greater confidence for predictions. 20

Intelligent DataBase System Lab, NCKU, Taiwan Geographic Score and Candidate Paths 21 User current movement: <Stay Location, Stay Location >User current movement: <Stay Location 3, Stay Location 0, Stay Location 1 > (Stay Location 0 (, ) (Stay Location 1, 1.0) (Stay Location 3, 0.667)(Stay Location 3, 0.667)(Stay Location 1, 0.667) (Stay Location 3, 0.667) (Stay Location 0, 0.7) (, ) (Stay Location 1, 1.0) (Stay Location 3, 0.667)(Stay Location 3, 0.667)(Stay Location 1, 0.667) (Stay Location 3, 0.667) Candidate pathsGeographicScore (Stay Location 3 )  (Stay Location 0 )  (Stay Location 1 ) 0

Intelligent DataBase System Lab, NCKU, Taiwan Geographic Score and Candidate Paths 22 (Stay Location 0 (, ) (Stay Location 1, 1.0) (Stay Location 3, 0.667)(Stay Location 3, 0.667)(Stay Location 1, 0.667) (Stay Location 3, 0.667) (Stay Location 0, 0. 7) (, ) (Stay Location 1, 1.0) (Stay Location 3, 0.667)(Stay Location 3, 0.667)(Stay Location 1, 0.667) (Stay Location 3, 0.667) Candidate pathsGeographicScore (Stay Location 0 )  (Stay Location 1 ) 0.8 × = 1.2 User current movement: <, Stay Location>User current movement: < Stay Location 0, Stay Location 1 >

Intelligent DataBase System Lab, NCKU, Taiwan Geographic Score and Candidate Paths 23 (Stay Location 0 (, ) (Stay Location 1, 1.0) (Stay Location 3, 0.667)(Stay Location 3, 0.667)(Stay Location 1, 0.667) (Stay Location 3, 0.667) (Stay Location 0, 0.7) (, ) (Stay Location 1, 1.0) (Stay Location 3, 0.667)(Stay Location 3, 0.667)(Stay Location 1, 0.667) (Stay Location 3, 0.667) Candidate pathsGeographicScore (Stay Location 1 )1.0 User current movement: <> Stay Location 1 >

Intelligent DataBase System Lab, NCKU, Taiwan Candidate Paths Transformation 24 α=0.8 Candidate pathsGeographicScore (Stay Location 0 )  (Stay Location 1 ) 0.8 × = 1.2 (Stay Location 1 )1.0 Candidate PathsSemantic Candidate Paths (Stay Location 0 )  (Stay Location 1 )(Unknown)  (School) (Stay Location 1 )(School)

Intelligent DataBase System Lab, NCKU, Taiwan Semantic Score 25 Semantic Candidate Seq.SemanticScore (Unknown)  (School) 0.8 × = 1.2 (School)1.0

Intelligent DataBase System Lab, NCKU, Taiwan Outline 26 Introduction Location Prediction Semantic Mining Geographic Mining Matching Strategy & Scoring Function Experiments Conclusions

Intelligent DataBase System Lab, NCKU, Taiwan Experiments 27 MIT reality mining dataset The Reality Mining project was conducted from at the MIT Media Laboratory 106 mobile users Trajectories Cell span Cell name

Intelligent DataBase System Lab, NCKU, Taiwan Experiments 28 Sensitivity Tests

Intelligent DataBase System Lab, NCKU, Taiwan Experiments 29 Impact of the semantic clustering

Intelligent DataBase System Lab, NCKU, Taiwan Experiments 30 Comparison of Prediction Strategies Geographic Only: GO Full-Matching: FM

Intelligent DataBase System Lab, NCKU, Taiwan Experiments 31 Efficiency Evaluation

Intelligent DataBase System Lab, NCKU, Taiwan Outline 32 Introduction Location Prediction Semantic Mining Geographic Mining Matching Strategy & Scoring Function Experiments Conclusions

Intelligent DataBase System Lab, NCKU, Taiwan Conclusions 33 A novel framework to predict the next location of a mobile user in support of various location-based services both semantic and geographic information A novel cluster-based prediction technique to predict the next location of a mobile user

Intelligent DataBase System Lab, NCKU, Taiwan Thank you for your attention Quetion? 34

Intelligent DataBase System Lab, NCKU, Taiwan MSTP-Similarity 35 Similarity of two users: P1…PmP1…Pm P1’…Pn’P1’…Pn’ There are m×n MSTP-Similarity user U user V

Intelligent DataBase System Lab, NCKU, Taiwan Semantic Trajectory Pattern 36 Semantic trajectory Geographic semantic information database a customized spatial database which stores the semantic information of landmarks that we collect via Google Map Frequent Pattern Prefix-Span

Intelligent DataBase System Lab, NCKU, Taiwan MSTP-Similarity 37 the ratio of common part

Intelligent DataBase System Lab, NCKU, Taiwan MSTP-Similarity Similarity of two patterns