Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Published byAnn Cameron Modified over 8 years ago

Similar presentations

Presentation on theme: "黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Exploring Spatial-Temporal Trajectory Model for Location."— Presentation transcript:

1 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Exploring Spatial-Temporal Trajectory Model for Location Prediction 2011.11.23 TMSG- Paper Reading

2 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Agenda Authors & Publication Paper Presentation My Comments 2

3 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Authors & Publication Wen-Chih Peng ( 彭文志 ) – http://people.cs.nctu.edu.tw/~wcpeng/ http://people.cs.nctu.edu.tw/~wcpeng/ – Advanced Database System Lab – http://db.csie.nctu.edu.tw/ http://db.csie.nctu.edu.tw/ – Best Student Paper Award IEEE MDM2011 – http://mdmconferences.org/mdm2011/ http://mdmconferences.org/mdm2011/ 3

4 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Paper Outline Introduction Related works Framework Model Prediction Experiments Conclusion 4

5 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

6 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Motivation 6 11:30? T1 勝出 !!

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

8 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

9 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ The framework of location prediction using STT model (contd.) 9

10 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

11 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Frequent region discovery Eps: the neighborhood number of a given radius MinTs: minimum number of points 11

12 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Trajectory transformation 12 MinSup = 6 !!

13 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Spatial-temporal trajectory model construction 13

14 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ STT model 14

15 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

16 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

17 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

18 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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 = 11:52 + 5 = 11:57 Minimum temporal error = |11:57-12:00|=3 Pro temporal = (2^2) / (3^2) = 0.44 18

19 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Location prediction 19

20 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Location prediction (contd.) 20 1 (1x1)

21 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Experiments Experimental setting Prediction accuracy comparison Storage requirements comparison Sensitivity analysis of parameters 21

22 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Experimental setting CarWeb – http://carweb.cs.nctu.edu.tw/carweb/ http://carweb.cs.nctu.edu.tw/carweb/ – Authors’ work published in 2008 – A real car trajectory dataset – Hsinchu city, Taiwan RunSaturday – http://www.runsaturday.com http://www.runsaturday.com – Collect training paths of sports hobbyists – Walk, run, bike 22

23 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

24 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

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

26 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Sensitivity analysis of parameters 26

27 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Sensitivity analysis of parameters (contd.) 27

28 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Sensitivity analysis of parameters (contd.) 28

29 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Sensitivity analysis of parameters (contd.) 29

30 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

31 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

32 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

33 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ My comments (contd.) Doubt~ ? – Frequent region detection:: Order issue vs. MinSup ? 33

34 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ 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

35 黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica http://angus-fuming-huang.blogspot.com/ Thanks for your listening……….. 35

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

Similar presentations

Decision Tree Evolution using Limited number of Labeled Data Items from Drifting Data Streams Wei Fan 1, Yi-an Huang 2, and Philip S. Yu 1 1 IBM T.J.Watson.

Decision Tree Evolution using Limited number of Labeled Data Items from Drifting Data Streams Wei Fan 1, Yi-an Huang 2, and Philip S. Yu 1 1 IBM T.J.Watson.
Principles of Density Estimation

Principles of Density Estimation
Mining User Similarity Based on Location History Yu Zheng, Quannan Li, Xing Xie Microsoft Research Asia.

Mining User Similarity Based on Location History Yu Zheng, Quannan Li, Xing Xie Microsoft Research Asia.
Moving Pattern Detection in Spatio Temporal Data Mining BY SEKHAR NALLURI VENGALA RAO PACHVA SURYA KOGANTI.

Moving Pattern Detection in Spatio Temporal Data Mining BY SEKHAR NALLURI VENGALA RAO PACHVA SURYA KOGANTI.
Mining Frequent Patterns II: Mining Sequential & Navigational Patterns Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.

Mining Frequent Patterns II: Mining Sequential & Navigational Patterns Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.
Mining Frequent Spatio-temporal Sequential Patterns

Mining Frequent Spatio-temporal Sequential Patterns
A Phrase Mining Framework for Recursive Construction of a Topical Hierarchy Date ： 2014/04/15 Source ： KDD’13 Authors ： Chi Wang, Marina Danilevsky, Nihit.

A Phrase Mining Framework for Recursive Construction of a Topical Hierarchy Date ： 2014/04/15 Source ： KDD’13 Authors ： Chi Wang, Marina Danilevsky, Nihit.
Frequent Closed Pattern Search By Row and Feature Enumeration

Frequent Closed Pattern Search By Row and Feature Enumeration
Yasuhiro Fujiwara (NTT Cyber Space Labs)

Yasuhiro Fujiwara (NTT Cyber Space Labs)
10 -1 Lecture 10 Association Rules Mining Topics –Basics –Mining Frequent Patterns –Mining Frequent Sequential Patterns –Applications.

10 -1 Lecture 10 Association Rules Mining Topics –Basics –Mining Frequent Patterns –Mining Frequent Sequential Patterns –Applications.
G. Alonso, D. Kossmann Systems Group

G. Alonso, D. Kossmann Systems Group
BOAT - Optimistic Decision Tree Construction Gehrke, J. Ganti V., Ramakrishnan R., Loh, W.

BOAT - Optimistic Decision Tree Construction Gehrke, J. Ganti V., Ramakrishnan R., Loh, W.
Patch to the Future: Unsupervised Visual Prediction

Patch to the Future: Unsupervised Visual Prediction
Yoshiharu Ishikawa (Nagoya University) Yoji Machida (University of Tsukuba) Hiroyuki Kitagawa (University of Tsukuba) A Dynamic Mobility Histogram Construction.

Yoshiharu Ishikawa (Nagoya University) Yoji Machida (University of Tsukuba) Hiroyuki Kitagawa (University of Tsukuba) A Dynamic Mobility Histogram Construction.
Learning on Probabilistic Labels Peng Peng, Raymond Chi-wing Wong, Philip S. Yu CSE, HKUST 1.

Learning on Probabilistic Labels Peng Peng, Raymond Chi-wing Wong, Philip S. Yu CSE, HKUST 1.
Constructing Popular Routes from Uncertain Trajectories Authors of Paper: Ling-Yin Wei (National Chiao Tung University, Hsinchu) Yu Zheng (Microsoft Research.

Constructing Popular Routes from Uncertain Trajectories Authors of Paper: Ling-Yin Wei (National Chiao Tung University, Hsinchu) Yu Zheng (Microsoft Research.
Calibration of an Infrared-based Automatic Counting System for Pedestrian Traffic Flow Data Collection Step 3: Calibration Models Calibration models were.

Calibration of an Infrared-based Automatic Counting System for Pedestrian Traffic Flow Data Collection Step 3: Calibration Models Calibration models were.
Report on Intrusion Detection and Data Fusion By Ganesh Godavari.

Report on Intrusion Detection and Data Fusion By Ganesh Godavari.
© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,

© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,
Presented by Ozgur D. Sahin. Outline Introduction Neighborhood Functions ANF Algorithm Modifications Experimental Results Data Mining using ANF Conclusions.

Presented by Ozgur D. Sahin. Outline Introduction Neighborhood Functions ANF Algorithm Modifications Experimental Results Data Mining using ANF Conclusions.

Similar presentations

Ads by Google