mining object movement patterns from trajectory data

MINING OBJECT MOVEMENT PATTERNS

FROM TRAJECTORY DATA

Phan Nhat Hai

4th October, 2013

Supervisors Dr. Dino Ienco, Pr. Pascal Poncelet, Dr. Maguelonne Teisseire

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

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)

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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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.


o1

o2o3 o4

[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 trajectoriesFor 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)


o1

o2

o3o4

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.


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.


MOTIVATIONS (1)

Motivations: Complexity? Are they enough? Informative patterns?

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dataInformative patterns

extract


MOTIVATIONS (2)

Proposed solution

data


Unifying

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OUTLINE

Background and Motivations

Unifying Framework

Gradual Trajectory

Mining Representative Movement Patterns

Conclusions and Perspectives

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CLUSTER MATRIX

Objects: transactions Clusters: items

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diaper

beer

diaperbeer


FREQUENT CLOSED ITEMSET FROM CLUSTER MATRIX

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Frequent Itemset


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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SWARM

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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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THE MAIN PROCESS (GET_MOVE)

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INCREMENTAL GET_MOVE

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THE MAIN PROCESS

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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


* http://iapg.jade-hs.de/personen/brinkhoff/generator/

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SWAINSONI

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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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OUTLINE


Unifying Framework

Gradual Trajectory



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ONE OF CLOSED SWARMS …

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o1

o2

o3

o4

o6

o5

c1 c2

c3

c4

c5

t1 t2 t3 t4 t5 t6


…GRADUAL TRAJECTORIES

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o1

o2

o3

o4

o6

o5

c1 c2

c3

c4

c5

t1 t2 t3 t4 t5 t6


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A CONCRETE EXAMPLE


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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TIME RELAXED GRADUAL TRAJECTORIES

Timestamps can be: non-consecutive within a sliding time window


o1

o2

o3

o4

c1c2

t1 t2 t3 ………… t999 t1000

A

F

Sliding window

Too far away

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Synthetic data: 500 objects - 10,000 timestamps Reasonable scalability Low complexity


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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

Convergent Divergent

OUTLINE


Unifying Framework

Gradual Trajectory



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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

CONTRIBUTIONS


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MOTIVATIONS

data

Patterns

1) One kind of patterns is not enough to describe the data!

2) Overlapping!


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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

MDL approach: LP(Odb) = L(P) + L(Odb|P)


ENCODING EXAMPLE (I)

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ENCODING EXAMPLE (II)

L(ODB|P) = 4 + 6 + 2 + 1 + 1 = 14 L(P) = 4 + 5 + 3 + 4 = 16LP(ODB) = 30

L(ODB|P) = 4 + 5 + 2 + 1 + 1 = 13L(P) = 4 + 5 = 9LP(ODB) = 22


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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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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

OUTLINE


Unifying Framework

Gradual Trajectory



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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


data

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OVERALL CONCLUSIONS (2) DEMONSTRATION SYSTEM

Link: http://www.lirmm.fr/~phan/multimove.jsp


http://www.lirmm.fr/~phan/multimove.jsp

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OVERALL CONCLUSIONS (3) – OTHER APPLICATIONS

Mining trajectories on genes

Mining trajectories on tweets


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PERSPECTIVES (1)

Streaming GeT_Move Mining representative movement patterns from streaming

trajectory data


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PERSPECTIVES (2)

Trajectory mining on remote sensing, spatial information on satellite image processing

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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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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.

mining object movement patterns from trajectory data

Data & Analytics