黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica TrajPattern: Mining Sequential Patterns from Imprecise Trajectories of Mobile Objects 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 Jiong Yang ( 杨炯 ) – Meng Hu ( 胡萌 ) – Case Western Reserve University – EECS – EDBT 2006 (International Conference on Extending Database Technology) – Springer LNCS 3896 – 3

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Introduction Digital trajectory appearance – Mobile devices – Global positioning system – Trajectories of mobile objects Moving pattern applications – Location prediction – Location-based commerce advertisement – Animals migration 4

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Introduction (contd.) Trajectory data is often imprecise – Energy consumption reducing – GPS miss – It is hard to formulate the pattern Goal – Not a particular prediction model, but rather a general data mining framework – Can be applied to existing location prediction methods 5

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Introduction (contd.) Sequential pattern model – Practical and popular in trajectory mining Method – Normalized match measure (NM) Apriori property (x) Min-max property (o) A new mining algorithm: TrajPattern – Growing process – Pruning process Pattern groups 6

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Preliminaries and Problem Statement Problem – Identifying sequential patterns of imprecise trajectories of mobile devices Assumption – A server and a set of mobile devices – Be capable to know their own locations (e.g. via GPS) – Asynchronously report locations 7

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Location Reporting Scheme A general pattern mining framework – Various different location inference models Location prediction method property – Given time, predicted location, actual location, certain distribution Predicted position – Without loss of generality… 8

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Location and Velocity Trajectories Synchronous snapshots – A consistent view of all objects Location trajectory – Mean & Standard deviation of the distribution of the true location of object o at ith snapshot Difference of the mean & standard deviation Velocity trajectory – The same as the above concepts 9

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Model of Trajectory Pattern Trajectory pattern, – p i is location Indifferent parameter, δ – Object is at most δ away from coordinate (x, y) Indifferent probability, – How likely an object is truly very close to a position p Match, between a pattern P and a trajectory T’ Normalized Match, 10

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Definition of Pattern Group Definition 1: Similar patterns – No larger than a pre-defined value, ϒ – ϒ, the maximum similar pattern distance Definition 2: Pattern group Problem Statement – For a given set of imprecise trajectories – To find k patterns with the most normalized match – Represented via the concept of pattern groups 11

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Properties of Trajectory Patterns Definition 3: Super-pattern – Contain relationship – Super-pattern, sub-pattern, proper super-pattern, proper sub-pattern Definition 4: i-trajectory pattern – Include i positions Property 1: Min-max property – Proof… 12

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica TrajPattern Algorithm Observations – Discovered trajectory patterns is usually shorter – Dynamically increasing NM threshold – Seed patterns based on the min-max property Procedures 1.Grid partition Singular patterns 2.Pattern generation Low and high patterns 3.Pruning 1-extension property 4.Pattern groups discovery Length clustering 13

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica 1-extension property 14

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica 1-extension property (contd.) The reason that we only need to retain the set of low patterns satisfying the 1-extension property… – So! Let’s remove all low patterns that do not satisfy this property 15

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Example of the Pattern Groups Discovery S1: (p 1, p 3, p 4, p 5 ), (p 2, p 6 ) S2: (p’ 1, p’ 3, p’ 6 ), (p’ 2, p’ 4 ), (p’ 5 ) – The smallest snapshot group: (p’ 5 ) – Extract 1-pattern: (p 5 ) S1: (p 1, p 3, p 4 ), (p 2, p 6 ) S2: (p’ 1, p’ 3, p’ 6 ), (p’ 2, p’ 4 ) – The smallest snapshot group: (p 2, p 6 ) – Extract 1-pattern: (p 2 ), (p 6 ), (p 4 ) S1: (p 1, p 3 ) S2: (p’ 1, p’ 3 ) – Extract 2-pattern: (p 1, p 3 ) Pattern groups: (p 2 ), (p 4 ), (p 5 ), (p 6 ), (p 1, p 3 ) 16

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Correctness Analysis 17

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Complexity Analysis Time complexity = O(k 2 M 2 NG) Space complexity = O(kMG) – k, user needed top patterns – G, the number of grids – M, the maximum length of a pattern – N, the size of the input trajectory data set 18

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Effectiveness of the NM Model Comparison of three prediction modules – Linear model (LM) – Linear Kalman Filter (LMF) – Recursive motion function (RMF) Real data set – 50 buses, 500 traces, 100 snapshots, weekday – Training: 450 ; Testing: 50 19

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Effectiveness of the NM Model (contd.) 20% to 40% vs. 10% to 20% 20

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Scalability and Sensitivity Baseline : InfoMiner – a projection based approach (PB) Synthetic data set – ZebraNet project (Kenya) Modification – Simulate some groups – Simulate some leaving – Simulate some moving distance and direction 21

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Scalability and Sensitivity (contd.) Scalability – The number of patterns needed, K – The number of sequences, S – The average length of a sequence, L – The various number of grids, G 22 Sensitivity – The effect of the indifferent threshold, δ

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Conclusion Mining trajectory patterns from a set of imprecise trajectories – Min-max property – TrajPattern algorithm – Pattern group Demonstrating the usefulness and efficiency – Real and synthetic data sets 23

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica My Comments Nice~ – The formal definitions are beautiful – Resources of experiments are various and living – Breakthrough of his previous research Weak~ – Do not explain the setting and applying of the three compared model clearly – Research scope is a little rough and inconsistent General DM framework vs. Specific sequential pattern 24

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica My Comments (contd.) Others~ – [Yang 2001] is worth to read InfoMiner: Mining Surprising Periodic Patterns 57 cites vs. 33 cites – If no certain distribution !!! What will happen ??? (pp.8) 25

黃福銘 (Angus F.M. Huang) ANTS Lab, IIS, Academia Sinica Thanks for your listening……….. 26