Exploring Spatial-Temporal Trajectory Model for Location Prediction
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Transcript of Exploring Spatial-Temporal Trajectory Model for Location Prediction
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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
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/2
Agenda• Authors & Publication• Paper Presentation• My Comments
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/3
Authors & Publication• Wen-Chih Peng (彭文志 )
– http://people.cs.nctu.edu.tw/~wcpeng/ – Advanced Database System Lab– http://db.csie.nctu.edu.tw/ – Best Student Paper Award
• IEEE MDM2011– http://mdmconferences.org/mdm2011/
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/4
Paper Outline• Introduction • Related works• Framework• Model• Prediction • Experiments• Conclusion
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/5
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
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/6
Motivation11:30?
T1勝出 !!
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/7
Related works• Next movement
– Markov chain– Motion functions
• Granularity problem– Density-based– Grid-based
• Pattern recognition– Trajectory mining
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/8
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
PST
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/9
The framework of location prediction using STT model (contd.)
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/10
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: ik+1 = (mean, sd)
– MinSup: minimal support segment count in a region– Object moving time: Gaussian distribution
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/11
Frequent region discovery
• Eps: the neighborhood number of a given radius• MinTs: minimum number of points
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/12
Trajectory transformation
MinSup = 6 !!
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/13
Spatial-temporal trajectory model construction
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/
STT model
14
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/15
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
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/16
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 nk of STT and the moving sequence sq
– i is the longest common suffix of nk and sq
– The more recent movements have greater effect on future movements
• Sq =abc ; Patterns: a(0.07), b(0.27), c(0.64), bc(0.91), ab(0.34)
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/17
Moving potential• To calculate the next movement candidates of the
best similar node located
• Measuring the spatial and temporal relationship simultaneously
– Prospatial : Conditional probability
– Protemporal : Chebyshev’s inequality 2
11)(
kkXP
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/18
Moving potential (contd.)
• Arrival time te = current time tc + average transition interval mean
• Temporal error: Minimum difference of te and the representative time tk+1 of next movement candidates
• Example:
• Next movement of nk: ik+1=(5,2)
• tk+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
• Protemporal = (2^2) / (3^2) = 0.44
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/19
Location prediction
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/20
Location prediction (contd.)
1 (1x1)
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/21
Experiments• Experimental setting• Prediction accuracy comparison• Storage requirements comparison• Sensitivity analysis of parameters
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/22
Experimental setting• 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– Collect training paths of sports hobbyists– Walk, run, bike
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/23
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
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/24
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
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/25
Storage requirements comparison
• HPM dramatically grows with the MinTs• STT using data structure of suffix tree can compress the
number of sequential patterns
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/26
Sensitivity analysis of parameters
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/27
Sensitivity analysis of parameters (contd.)
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/28
Sensitivity analysis of parameters (contd.)
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/29
Sensitivity analysis of parameters (contd.)
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/30
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
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/31
My Comments• Strengths~
– Well paper structure– Well representative illustrations– Abundant experiments
• Accuracy + storage + sensitivity– Transition probability + Appearing probability
• Be a more sophisticated trajectory formation
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/32
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”
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/33
My comments (contd.)
• Doubt~– Frequent region detection:: Order issue vs. MinSup ?
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/34
My comments (contd.)
• Insight~– Different mobility modes reflect different movement patterns number
• Arbitrary vs. Limited• Different prediction design
– Reduce patterns number– Promote prediction accuracy
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黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica
http://angus-fuming-huang.blogspot.com/35
Thanks for your listening………..