The document discusses methods for mining movement patterns from trajectory data using a unifying framework. It presents various algorithms such as get_move to efficiently extract movement patterns like swarms and convoys, emphasizing the importance of incremental updates and pattern representation. The work aims to improve data compression and pattern discovery in spatio-temporal databases, with applications in areas like route planning and animal behavior analysis.

1. MINING OBJECT MOVEMENT PATTERNS
FROM TRAJECTORY DATA
Phan Nhat Hai
4th October, 2013
Supervisors
Dr. Dino Ienco, Pr. Pascal Poncelet, Dr. Maguelonne Teisseire

2. BACKGROUND AND MOTIVATION
 Nowadays, many electronic devices are used for real
world applications
 GPS, sensor networks, mobile phone, …
 « interesting » patterns for:
 movement pattern analysis, animal behavior, route
planning and vehicle control, location prediction, …
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

3. SOME EXAMPLES
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Route Planning
Animal migration analysis
-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
The world’s largest traffic jam
in history (China)

4. SPATIO-TEMPORAL DATA (ST)
 Represented as a list of points, located in space and
time
 T=(x1,y1, t1), …, (xn, yn, tn)  position in space at time ti
was (xi, yi)
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

5. MINING SPATIO-TEMPORAL PATTERNS FROM TRAJECTORY
DATA (1)
 Frequent Patterns:
 Frequent followed paths:
 Group pattern [6], Tralus [7], …
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Region (Cluster)
[6] Y. Wang et. al. Data Knowl.
Eng., June 2006.
[7] J. G. Lee et. al. In ACM
SIGMOD ’07.
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
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6. [1] Z.Li et. al. PVLDB 10.
[2] P. Kalnis et. al. SSTD’05.
[3] J. Gudmundsson et. al. ACM GIS’06.
[4] H. Jeung et. al. VLDB 08.
[5] F. Verhein. SDM’09.
MINING SPATIO-TEMPORAL PATTERNS FROM TRAJECTORY
DATA (2)
 Clustering:
 Group together similar trajectories
 For each group produce a summary
 Flock [3], convoy [4], moving cluster [2], swarm & closed
swarm [1], k-Star [5]
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Region (Cluster)
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
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7. SWARM – CLOSED SWARM [1]
 Swarm - groups of objects (O, T ):
 At least objects move together
 timestamps
 Closed Swarm
 Swarm which cannot be enlarged
 Algorithm
 ObjectGrowth
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[1] Z.Li et. al. Swarm: mining
relaxed temporal moving object
clusters. PVLDB 2010.
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

8. CONVOY [4]
 Convoy - groups of objects (O, T ):
 At least objects move together
 consecutive timestamps
 Algorithm
 CuTS*
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[4] H. Jeung et. al. Discovery of
convoys in trajectory
databases. PVLDB 2008.
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

9. MOTIVATIONS (1)
 Motivations:
 Complexity?
 Are they enough?
 Informative patterns?
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data
Informative
patterns
extract
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

10. MOTIVATIONS (2)
 Proposed solution
data
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
Unifying
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11. OUTLINE
 Background and Motivations
 Unifying Framework
 Gradual Trajectory
 Mining Representative Movement Patterns
 Conclusions and Perspectives
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12. CLUSTER MATRIX
 Objects: transactions
 Clusters: items
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diaper
beer
diaperbeer
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

13. FREQUENT CLOSED ITEMSET FROM CLUSTER
MATRIX
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Frequent Itemset
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

14. THE MAIN INTUITION (FOLLOWING…)
 We are now able to extract itemsets corresponding to
a set of clusters occurring over time
 Not movement patterns yet!
 What about properties on Itemsets?
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

15. SWARM
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

16. PROPERTIES
 In the same way it is possible to define properties for:
Swarm, Closed Swarm, Convoy, Moving Cluster,
Periodic Pattern, …
 We are now able to extract different movement
patterns!
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

17. THE MAIN PROCESS (GET_MOVE)
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

18. INCREMENTAL GET_MOVE
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

19. THE MAIN PROCESS
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

20. CMC CuTS* ObjectGrowth Vg-Growth Incremental
GeT_Move
Convoys X X X
Closed Swarms X X
Group Patterns X X
Moving Cluster X
EXPERIMENTAL RESULTS
 Datasets:
 Competitive algorithms:
#objects #timestamps
Swainsoni 43 4,425
Buffalo 165 3,000
Synthetic* 500 10,000
Synthetic 2 50,000 10,000
- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
* http://iapg.jade-hs.de/personen/brinkhoff/generator/
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21. SWAINSONI
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

22. UNIFYING FRAMEWORK – CONCLUSIONS
 GeT_Move: a unifying movement pattern mining
approach
 Properties adapted to specific movement patterns
 Proofs of properties
 Theorem providing that all the patterns are found
 Incremental GeT_Move
 A new approach for identifying the size of blocks
 Fully nested block partition
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

23. OUTLINE
 Background and Motivations
 Unifying Framework
 Gradual Trajectory
 Mining Representative Movement Patterns
 Conclusions and Perspectives
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24. ONE OF CLOSED SWARMS …
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

25. …GRADUAL TRAJECTORIES
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c1 c2
c3
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c5
t1 t2 t3 t4 t5 t6
-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

26. A CONCRETE EXAMPLE
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

27. PATTERN DEFINITION
 The objects still remain in the next cluster
 The number of objects is equal-increasing (resp. equal-
decreasing)
 At least a number of certain timestamps
 non-consecutive
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- Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
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28. TIME RELAXED GRADUAL TRAJECTORIES
 Timestamps can be:
 non-consecutive
 within a sliding time window
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
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c1
c2
t1 t2 t3 ………… t999 t1000
A
F
Sliding
window
Too far away

29. EXPERIMENTAL RESULTS
 Synthetic data: 500 objects - 10,000 timestamps
 Reasonable scalability
 Low complexity
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

30. GRADUAL TRAJECTORY - CONCLUSIONS
 New kinds of trajectories: gradual trajectory
 ClusterGrowth: an efficient algorithm to extract all
gradual trajectories
 Fuzzy closed swarm
 Too many extracted patterns:
 DiCompoGP algorithm to directly extract the top-k gradual
trajectories 30
Convergent Divergent

31. OUTLINE
 Background and Motivations
 Unifying Framework
 Gradual Trajectory
 Mining Representative Movement Patterns
 Conclusions and Perspectives
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32.  Methodology
 A set of movement patterns (closed swarms, convoys,
gradual trajectories, etc.)
 Employ MDL (Minimum Description Length) schema to
select the most informative and less redundant pattern
set
 Compo Algorithm
 Rank and select the most representative patterns
 Allow different types of pattern in the final results
 Characterize data by the selected patterns
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CONTRIBUTIONS
-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

33. MOTIVATIONS
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data
Patterns
1) One kind of patterns is not enough
to describe the data!
2) Overlapping!
-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

34. PROBLEM STATEMENT
 Given a spatio-temporal DB Odb and a set of patterns F
(extracted from Odb)
 Discover the optimal dictionary P (subset of F)
 compresses the data best w.r.t. the given encoding schema
 L(p): number of bits to encode the pattern p + extra bit
to encode the type of pattern
 L(Odb|P): number of bits to encode the dataset Odb
given P
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MDL approach: LP(Odb) = L(P) + L(Odb|P)
-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

35. ENCODING EXAMPLE (I)
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36. ENCODING EXAMPLE (II)
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L(ODB|P) = 4 + 6 + 2 + 1 + 1 = 14
L(P) = 4 + 5 + 3 + 4 = 16
LP(ODB) = 30
L(ODB|P) = 4 + 5 + 2 + 1 + 1 = 13
L(P) = 4 + 5 = 9
LP(ODB) = 22
-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

37. NAÏVE COMPO VS SMART COMPO
We design two different approaches:
 Naive Compo (baseline)
 Work in a greedy way
 Given the actual P, for each candidate p’ recompress the data
with P U p’
 Select the p’ that obtain the best performance
 Smart Compo
 Compute the gain incrementally
 Avoid to recompress the whole data
 Directly compute Gain(p’,P) = L(Odb|P) - L(Odb|P U p’) without
compute L(Odb|P U p’)
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

38. EXPERIMENTAL RESULTS
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

39. REPRESENTATIVE PATTERN - CONCLUSIONS
 Propose an encoding scheme allowing multi-
overlapping movement patterns
 Propose two algorithms
 Naïve Compo (greedy approach)
 Smart Compo (compute gain incrementally)
 Experimental results show that the top-k
representative patterns are well adapted to the data
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40. OUTLINE
 Background and Motivations
 Unifying Framework
 Gradual Trajectory
 Mining Representative Movement Patterns
 Conclusions and Perspectives
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41. OVERALL CONCLUSIONS (1)
 Three step framework
 GeT_Move: a unifying movement pattern mining approach
 Discovering novel patterns: Gradual trajectory + Fuzzy
closed swarm
 Mining representative movement patterns
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives
data

42. OVERALL CONCLUSIONS (2)
DEMONSTRATION SYSTEM
 Link: http://www.lirmm.fr/~phan/multimove.jsp
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

43. OVERALL CONCLUSIONS (3) – OTHER APPLICATIONS
 Mining trajectories on genes
 Mining trajectories on tweets
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

44. PERSPECTIVES (1)
 Streaming GeT_Move
 Mining representative movement patterns from streaming
trajectory data
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

45. PERSPECTIVES (2)
 Trajectory mining on remote sensing, spatial
information on satellite image processing
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46. EXTRA WORK
 Mining multi-relational gradual patterns (with Prof. Donato
Malerba)
 Kendal’s tau
 Gradual support
 Communication graph summarization (with Dr. Francesco
Bonchi)
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-Background and Motivations - Unifying Framework - Gradual Trajectory - Mining Representative Movement Patterns - Conclusions & Perspectives

47. PUBLICATIONS
[1] P. N. Hai, D. Ienco, P. Poncelet, M. Teisseire. "Mining Representative Movement Patterns
through Compression". PAKDD 2013.
[2] A.Z.E. Aabidine, A. Sallaberry, S. Bringay, M. Fabregue, C. Lecellier, P. N. Hai, P. Poncelet.
“Co2Vis: A Visual Analytics Tool for Mining Co-expressed And Co-regulated Genes Implied
in HIV Infections”. IEEE BioVis 2013.
[3] P. N. Hai, D. Ienco, P. Poncelet, M. Teisseire. "Mining Fuzzy Moving Object Clusters".
ADMA 2012.
[4] P. N. Hai, D. Ienco, P. Poncelet, M. Teisseire. "Mining Time Relaxed Gradual Moving Object
Clusters". ACM GIS 2012.
[5] F. Bouillot, P. N. Hai, N. Béchet, S. Bringay, D. Ienco, S. Matwin, P. Poncelet, M. Roche,
and M. Teisseire. "How to Extract Relevant Knowledge from Tweets?". ISIP 2012.
[6] P. N. Hai, P. Poncelet, M. Teisseire. "GET_MOVE: An Efficient and Unifying Spatio-
Temporal Pattern Mining Algorithm for Moving Objects". IDA 2012.
[7] P. N. Hai, P. Poncelet, M. Teisseire. "An Efficient Spatio-Temporal Mining Approach to
Really Know Who Travels with Whom!". BDA 2012. (selected as Best papers)
[8] P. N. Hai, D. Ienco, P. Poncelet, M. Teisseire. "Extracting Trajectories through an Efficient
and Unifying Spatio-Temporal Patten Mining System". ECML-PKDD 2012.
[9] P. N. Hai, P. Poncelet, M. Teisseire. "MovingObjects: Combining Gradual Rules and Spatio-
Temporal Patterns". IEEE ICSDM 2011.
[10] P. N. Hai, P. Poncelet, M. Teisseire. "An Efficient Spatio-Temporal Mining Approach to
Really Know Who Travels with Whom!". ISI special issue, selected papers from BDA’12,
2013, to appear. 47