Where Next? Data Mining Techniques and …lxiong/cs570/share/slides/mobility.pdfPattern-based...
Transcript of Where Next? Data Mining Techniques and …lxiong/cs570/share/slides/mobility.pdfPattern-based...
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Where Next? Data Mining Techniques and Challenges for
Trajectory Prediction
Slides credit: Layla Pournajaf
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o Navigational services.
o Traffic management.
o Location-based advertising.
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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o Destination prediction
o Path prediction with known destination
o Path prediction with unknown destinationoSimilar to predicting next N locations
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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Raw Trajectories
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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Raw Trajectories
Preprocessed Trajectories
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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Raw Trajectories
Preprocessed Trajectories
Prediction Model
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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Real-world data include raw trajectories of continuous GPS coordinates which are noisy and inaccurate!
Source: www.openstreetmap.org
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Raw Trajectories
Preprocessed Trajectories
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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o Discretizing Timeo30 seconds, one hour
oTemporal Representationo Location-series
o Fixed-interval time-location series
o Variable-interval time-location
series
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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o Discretizing LocationoGrid-based (uniform vs
hierarchical)
oMining Frequent RegionsClustering
Semantic-based
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.
Map of Beijing with 30 × 30 grid overlay: Each cell ≈ 1.78km2
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o Clusteringo DBScan
o Hierarchical Clustering
o Semantic-basedo Using points of interests
Source: Lei, Po-Ruey, et al. "Exploring spatial-temporal trajectory model for location prediction." MDM 2011.
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Raw Trajectories
Preprocessed Trajectories
Prediction Models
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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Personalized / Individual-based:o Utilize only the history of one object to predict its future locations
General:o Utilize the history of all objects to predict future locations
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Model-based (formulate the movement of moving objects using mathematical models)o Markov Models
o Hidden Markov Models (Zhou et. al., ACM SIGKDD 2013)
o Recursive Motion Function (Y. Tao et. al., ACM SIGMOD 2004)
oDeep learning models Pattern-based (exploit pattern mining algorithms for prediction)
o Sequential Pattern Mining (G. Yavas et. al., DKE 2005)
o Trajectory Pattern Mining
Hybrido Recursive Motion Function + Sequential Pattern Mining (H. Jeung et. al.,
ICDE 2008)
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Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.
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2𝑝45 = 3
1𝑝56 = 3
Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.
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Source: Xue, Andy Yuan, et al. "Destination prediction by sub-trajectory synthesis and privacy protection against such prediction." ICDE 2013.
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Partial Trajectory:<𝑟1, 𝑡1> , <𝑟2, 𝑡2>, …., <𝑟𝑐, 𝑡𝑐>
Prediction:• Having a partial trajectory (discretized) including the current
region 𝑟𝑐, find the most probable region at time point 𝑡𝑐+1
arg max P(𝑅𝑐+1 = 𝑟𝑐+1| 𝑟1 , … 𝑟𝑐) 𝑟𝑐+1
<?, 𝑡𝑐+1>
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Embedding Higher-Order Chains
• Each new state depends on fixed-length
window of preceding state values
• We can represent this as a first-order model
via state augmentation:
(N2 augmented states)
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Semi-Lazy Hidden Markov Approach (SIGKDD ‘13)
• Find similar trajectories from historical trajectories (reference objects)
• Build a hidden Markov Model on the fly (vs. eager or lazy approach)
• Self-correcting continuous prediction (real time)• Refine prediction model• Adjust weights for reference objects
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Model-based (formulate the movement of moving objects using mathematical models)o Markov Models
o Hidden Markov Models (Zhou et. al., ACM SIGKDD 2013)
o Recursive Motion Function (Y. Tao et. al., ACM SIGMOD 2004)
oDeep learning models Pattern-based (exploit pattern mining algorithms for prediction)
o Sequential Pattern Mining (G. Yavas et. al., DKE 2005)
o Trajectory Pattern Mining (Monreale et al ACM SIGKDD 2009)
Hybrido Recursive Motion Function + Sequential Pattern Mining (H. Jeung et. al.,
ICDE 2008)
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1. Preprocess raw trajectories and
extract frequent sequential patterns
(T-Pattern)
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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1. Preprocess raw trajectories and
extract frequent sequential patterns
(T-Pattern)
2. Build a Prefix Tree (T-Pattern Tree)
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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1. Preprocess raw trajectories and
extract frequent sequential patterns
(T-Pattern)
2. Build a Prefix Tree (T-Pattern Tree)
3. Predict Next Location
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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<𝑥1, 𝑦1, 𝑡1> , <𝑥2, 𝑦2, 𝑡2>, …., <𝑥𝑛, 𝑦𝑛, 𝑡𝑛>
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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• Two points match if one falls within a spatial neighborhood N()of the other
• Two transition times match if their temporal difference is ≤ τ
<𝑥1, 𝑦1, 𝑡1> , <𝑥2, 𝑦2, 𝑡2>, …., <𝑥𝑛, 𝑦𝑛, 𝑡𝑛>
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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• Two points match if one falls within a spatial neighborhood N()of the other
• Two transition times match if their temporal difference is ≤ τ
<𝑥1, 𝑦1, 𝑡1> , <𝑥2, 𝑦2, 𝑡2>, …., <𝑥𝑛, 𝑦𝑛, 𝑡𝑛>
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
• Calculate support for each T-pattern
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Generating all association rules from each T-pattern and using them to build aclassifier is too expensive.
R1 R2 R3 R4T-Pattern
Rules R1 R2 R3 R4
R1 R2 R3 R4
R1 R2 R3 R4
α1 α2α3
Source: A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti. WhereNext: a Location Predictor on Trajectory Pattern Mining. KDD 2009
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To avoid the rules generation, the T-Pattern set is organized as a prefix tree.
For Each node v
•Id identifies the node v
•Region is a spatial component of the T-Pattern
•Support is the support of the T-pattern
For Each edge j
[a,b] correspond to the time interval αn of the T-Pattern
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Three steps:1. Search for best match
2. Candidate generation
3. Make predictions
Best Match
Prediction
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Three steps:1. Search for best match
2. Candidate generation
3. Make predictions
The Best Match is the path having: the maximum path score using the time and location matching
and support at least one admissible prediction.
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o Prediction errors (distance and time)
o Prediction accuracy (precision and recall)
o Prediction rate
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H. Jeung, Q. Liu, H. T. Shen, and X. Zhou. A hybrid prediction model for moving objects. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on, pages 70-79. IEEE, 2008.
J. Krumm and E. Horvitz. Predestination: Inferring destinations from partial trajectories. In Ubiquitous Computing, pages 243-260. Springer, 2006. A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. Wherenext: a location predictor on trajectory pattern mining. In Proceedings of the 15th ACM SIGKDD
international conference on Knowledge discovery and data mining, pages 637-646. ACM, 2009. G. Yavas, D. Katsaros, O. Ulusoy, and Y. Manolopoulos. A data mining approach for location prediction in mobile environments. Data & Knowledge
Engineering, 54(2):121-146, 2005. Y. Tao, C. Faloutsos, D. Papadias, and B. Liu. Prediction and indexing of moving objects with unknown motion patterns. In Proceedings of the 2004 ACM
SIGMOD international conference on Management of data, pages 611-622. ACM, 2004. A. Y. Xue, R. Zhang, Y. Zheng, X. Xie, J. Huang, and Z. Xu. Destination prediction by sub-trajectory synthesis and privacy protection against such prediction.
In Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pages 254-265. IEEE, 2013.