黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Exploring Spatial-Temporal Trajectory Model for Location.

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Presentation transcript:

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Exploring Spatial-Temporal Trajectory Model for Location Prediction TMSG- Paper Reading

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Agenda Authors & Publication Paper Presentation My Comments 2

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Authors & Publication Wen-Chih Peng ( 彭文志 ) – – Advanced Database System Lab – – Best Student Paper Award IEEE MDM2011 – 3

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Paper Outline Introduction Related works Framework Model Prediction Experiments Conclusion 4

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Introduction Location prediction problem – Given an object’s recent movements and a future time, the location of this object at the future time is estimated 5

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Motivation 6 11:30? T1 勝出 !!

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Related works Next movement – Markov chain – Motion functions Granularity problem – Density-based – Grid-based Pattern recognition – Trajectory mining 7

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica The framework of location prediction using STT model Frequent region discovery – Sufficient number of data points Trajectory transformation – Region-based moving sequence STT model construction – Probabilistic suffix tree – Transition probability – Appearing probability 8PST

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica The framework of location prediction using STT model (contd.) 9

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Spatial-temporal trajectory model construction Frequent region discovery and trajectory transformation – Def. 1: Frequent Region – Def. 2: Region-based Moving Sequence Spatial-temporal trajectory model construction – Predictive table: spatial and temporal correlation between the region and next movement – Transition time interval: i k+1 = (mean, sd) – MinSup: minimal support segment count in a region – Object moving time: Gaussian distribution 10

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Frequent region discovery Eps: the neighborhood number of a given radius MinTs: minimum number of points 11

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Trajectory transformation 12 MinSup = 6 !!

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Spatial-temporal trajectory model construction 13

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica STT model 14

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Location prediction using STT model Prediction concept – To find the best next movement literally until the query time is reached Kernel methods – Movement similarity – Moving potential – Location prediction 15

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Movement similarity To search a best similar node between query sequence and STT node Measuring the similarity of a labeled sequence of a tree node n k of STT and the moving sequence s q – i is the longest common suffix of n k and s q – The more recent movements have greater effect on future movements S q =abc ; Patterns: a(0.07), b(0.27), c(0.64), bc(0.91), ab(0.34) 16

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Moving potential To calculate the next movement candidates of the best similar node located Measuring the spatial and temporal relationship simultaneously – Pro spatial : Conditional probability – Pro temporal : Chebyshev’s inequality 17

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Moving potential (contd.) Arrival time t e = current time t c + average transition interval mean Temporal error: Minimum difference of t e and the representative time t k+1 of next movement candidates Example: Next movement of n k : i k+1 =(5,2) t k+1 ={12:00, 15:00, 17:00} If the current time is 11:52 ================================ Arrival time = 11: = 11:57 Minimum temporal error = |11:57-12:00|=3 Pro temporal = (2^2) / (3^2) =

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Location prediction 19

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Location prediction (contd.) 20 1 (1x1)

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Experiments Experimental setting Prediction accuracy comparison Storage requirements comparison Sensitivity analysis of parameters 21

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Experimental setting CarWeb – – Authors’ work published in 2008 – A real car trajectory dataset – Hsinchu city, Taiwan RunSaturday – – Collect training paths of sports hobbyists – Walk, run, bike 22

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Prediction accuracy comparison E1: To verify the prediction accuracy of STT can be improved by using grid-based clustering approach – STT-Grid vs. STT-DBSCAN – Test 150 queries – Prediction error 23

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Prediction accuracy comparison (contd.) E2: Prediction performance comparison – STT vs. HPM (Hybrid Prediction Model) – An association rule-based pattern prediction approach – Under the various MinTs – Prediction error 24

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Storage requirements comparison HPM dramatically grows with the MinTs STT using data structure of suffix tree can compress the number of sequential patterns 25

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Sensitivity analysis of parameters 26

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Sensitivity analysis of parameters (contd.) 27

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Sensitivity analysis of parameters (contd.) 28

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Sensitivity analysis of parameters (contd.) 29

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Conclusion To discover frequent movement patterns To answer predictive queries To reduce the pattern storage size A spatial-temporal trajectory model – Capture an object’s moving behavior – Forecast its future locations 30

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica My Comments Strengths~ – Well paper structure – Well representative illustrations – Abundant experiments Accuracy + storage + sensitivity – Transition probability + Appearing probability Be a more sophisticated trajectory formation 31

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica My comments (contd.) Weaknesses~ – Too many repeated sentences – No future work suggestions – The definition / interval of the RECENT movement is vague – The sentence (assumption) needs to be verified (by experiments) “The more recent movements have greater effect on future movements” 32

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica My comments (contd.) Doubt~ ? – Frequent region detection:: Order issue vs. MinSup ? 33

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica My comments (contd.) Insight~ – Different mobility modes reflect different movement patterns number Arbitrary vs. Limited Different prediction design – Reduce patterns number – Promote prediction accuracy 34

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Thanks for your listening……….. 35