Semantic-based Trajectory Data Mining Methods

2

A importância de considerar a semântica

T1

T2T3

T4 T1

T2T3

T4

H

H

H

Hotel

R

R

R Restaurant

C

C

C Cinema

Padrão SEMÂNTICO

(a) Hotel p/ Restaurante, passando por SC

(b) Cinema, passando por SC

Padrão Geométrico

SC

Geometric Patterns X Semantic Patterns (Bogorny 2008)

There is very little or no semantics in most DM approaches for trajectories

Consequence:

• Patterns are purely geometrical

• Difficult to interpret from the user’s point of view

• Do not discover semantic patterns,

which can be independent of spatial location

4

Dados GeográficosTrajetórias Brutas (x,y,t)

Principal Problema: Falta de semântica

Geografia + Trajetória Bruta =Trajetória Semântica

Motivada por um Modelo Conceitualpara Trajetórias

6

Trajetória Metafórica (Spaccapietra 2008)

institution

Time

position

(Assistant, Paris VI, 1966-1972)

(Lecturer, Paris VI, 1972-1983)

(Professor, Dijon, 1983-1988)

(Professor, EPFL, 1988-2010)

begin

end

7 7

Modelagem Conceitual (EPFL, Suíça)

Primeiro modelo conceitual para trajetórias:

STOP: parte importante de uma trajetória do ponto de vista de uma aplicação, considerando as seguintes restrições:

durante um stop o objeto móvel é considerado parado

O stop tem uma duração (tf - ti > 0)

MOVE: parte da trajetória entre 2 stops consecutivos ou entre um stop e o início/fim da trajetória

The Model of Stops and Moves (Spaccapietra 2008)

STOPS

Important parts of trajectories

Where the moving object has stayed for a minimal amount of time

Stops are application dependent Tourism application

Hotels, touristic places, airport, …

Traffic Management Application Traffic lights, roundabouts, big events…

MOVES

Are the parts that are not stops

9

Traveler

location

Has

Trajectory

hasStops

Stop

Place

IsIn

0:N list

1:1

2:N list

1:1

0:N

0:N

Move ƒ(T)

To

From

0:11:1

1:10:1

Modelo de Stops e Moves

10

Adicionando semântica às trajetórias: usando STOPS

Aeroporto[08:00 – 08:30]

Ibis Hotel[10:00-12:00]]

Museu Louvre [13:00 – 17:00]

Torre Eifel[17:30 – 18:00]

1

2

3Congestionamento[09:00 – 09:15]

Rótula[08:40 – 08:45]

Aeroporto[08:00 – 08:30]

Cruzamento[12:15 – 12:22]

STOPS são dependentes da aplicação

Semantic Trajectories

A semantic trajectory is a set of stops and moves

Stops have a place, a start time and an end time

Moves are characterized by two consecutive stops

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Métodos para instanciar o modelo de stops e moves e minerar trajetórias semanticas

Methods to Compute Stops and Moves

1) IB-SMoT (INTERSECTION-based)

Interesting for applications like tourism and urban planning

2) CB-SMoT (SPEED-based clustering)

Interesting for applications where the speed is important,

like traffic management

3) DB-SMOT (DIRECTION-based clustering)

Interesting in application where the direction variation is important

like fishing activities

IB-SMoT (Alvares 2007a)

A candidate stop C is a tuple (RC, C), where

RC is the geometry of the candidate stop (spatial feature type)

C is the minimal time duration

E.g. [Hotel - 3 hours]

An application A is a finite set

A = {C1 = (RC1 , C1 ), …, CN = (RCN , CN)} of candidate stops with non-overlapping geometries RC1, … ,RCN

E.g. [Hotel - 3 hours, Museum – 1 hour]

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IB-SMoT

Input: candidate stops // Application

trajectories // trajectory samples

Output:

Method: For each trajectory

Check if it intersects a candidat stop for a minimal amount of time

Semantic rich trajectories

Jurere

09-12

FloripaS

16-17

IbisH.

13-14

(Alvares 2007ª)

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Schema of Stops and Moves

Tid Mid S1id S2id geometry timest

1 1 1 2 48.888880 2.246102 08:41

1 1 1 2 48.885732 2.255031 08:42 ... ... ... ... ... ...

1 1 1 2 48.860021 2.336105 09:04

1 2 2 3 48.860515 2.349018 09:41 ... ... ... ... …

...

1 2 2 3 48.861112 2.334167 10:00

Tid Sid SFTname SFTid Sbegint Sendt

1 1 Hotel 1 08:25 08:40

1 2 TouristicPlace 3 09:05 09:30

1 3 TouristicPlace 3 10:01 14:20

Id Name Stars geometry

1 Ibis 2 48.890015 2.246100, ...

2 Meridien 5 48.880005 2.283889, …

Id Name Type geometry

1 Notre Dame Church 48.853611 2.349167,…

2 Eiffel Tower Monument 48.858330 2.294333,…

3 Louvre Museum 48.862220 2.335556,…

Stops

Moves

Touristic PlaceHotel

Alvares (ACM-GIS 2007)

17

17

Q2: How many trajectories go from a Hotel to at least one Touristic Place?

SELECT distinct count(t.Tid)

FROM trajectory t, trajectory u, hotel h, touristicPlace p

WHERE intersects (t.geometry, h.geometry) AND

Intersects (u.geometry, p.geometry) AND

t.Tid=u.Tid AND u.timest>t.timestSELECT distinct count(a.Tid)

FROM stop a, stop b

WHERE a.SFTname='Hotel' AND

b.SFTname='Touristic Place' AND a.Tid=b.Tid

AND a.Sid < b.Sid

No Spatial Join

Trajectory samples

Semantic Trajectories

Alvares (ACM-GIS 2007)

Queries: Trajectory Samples X Stops and Moves

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18

Queries: Trajectory Samples X Stops and Moves

SELECT ‘Hotel’ as place

FROM trajectory t, hotel h

WHERE t.id='A' AND

intersects (t.movingpoint.geometry,h.geometry)

UNION

SELECT ‘TouristicPlace’ as place

FROM trajectory t, touristicPlace p

WHERE t.id='A' AND

intersects (t.movingpoint.geomtetry,p.geometry)

UNION

…

SELECT SFTname as place

FROM stop

WHERE id='A‘

Q1: Which are the places that moving object A has passed during his trajectory?

Alvares (ACM-GIS 2007)

19

19

Q2: How many trajectories go from a Hotel to at least one Touristic Place?

SELECT distinct count(t.Tid)

FROM trajectory t, trajectory u, hotel h, touristicPlace p

WHERE intersects (t.geometry, h.geometry) AND

Intersects (u.geometry, p.geometry) AND

t.Tid=u.Tid AND u.timest>t.timestSELECT distinct count(a.Tid)

FROM stop a, stop b

WHERE a.SFTname='Hotel' AND

b.SFTname='Touristic Place' AND a.Tid=b.Tid

AND a.Sid < b.Sid

No Spatial Join

Trajectory samples

Semantic Trajectories

Alvares (ACM-GIS 2007)

Queries: Trajectory Samples X Stops and Moves

20

20

Q4: Which are the Touristic Places that moving objects have passed and stayed

for more than one hour?

SELECT temp.name, count(*) AS n_visits

FROM ( SELECT t.Tid, p.name

FROM trajectory t, touristicplace p

WHERE intersects (t.geometry,p.geometry)

GROUP BY t.Tid, p.name

HAVING count(t.*)>60) AS temp

GROUP BY temp.name

SELECT t.name, count(s.*) AS n_visits

FROM stop s, touristicplace p

WHERE s.SFTid=p.id AND (s.Sendt - s.Sbegint ) > 60

GROUP BY t.name

No Spatial Join

Alvares (ACM-GIS 2007)

Queries: Trajectory Samples X Stops and Moves

Input: Trajectory samples

Speed variation

minTime

Output: stops and moves

Step 1: find clusters Step 2: Add semantics to each cluster

2.1: If intersects during t stop

Jurere

09-12

FloripaS

16-17

IbisH.

13-14

Unknown stop

2.2: If no intersection

during t unknown stop

CB-SMoT: Speed-based clustering (Palma 2008)

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Tutorial on Spatial and Spatio-Temporal Data Mining (ICDM 2010)

22

22

Stops (Methods SMot and CB-SMoT)

DB-SMOT : Direction-based Clustering (Manso 2010)

Input: trajectories // trajectory samples

minDirVariation // minimal direction variation

minTime // minimum time

maxTolerance

Output: semantic rich trajectories

Method:

For each trajectory

Find clusters with direction variation

higher than minDirVariation

For a minimal amount of time

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Resultados obtidos com os Métodos que Agregam Semântica – Trajetórias de Barcos de Pesca

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Resultados obtidos com os Metodos que Agregam Semântica – Trajetórias de Barcos de Pesca

Works Summarized in this part of the Tutorial

Geometric Pattern Mining Methods (mining is on sample points)

Semantic Pattern Mining Methods (Generate Semantic Trajectories using DM - mining is on Semantic Trajectories)

Behaviour Pattern Mining and Interpretation Methods

Laube 2004, 2005

Hwang 2005

Gudmundson 2006, 2007

Giannotti 2007

Lee 2007

Cao 2006, 2007

Lee 2007, 2008a, 2008b

Li 2010

Alvares 2007

Zhou 2007

Palma 2008

Bogorny 2009

Bogorny 2010

Manso 2010

Alvares 2010

Giannotti 2009

Baglioni 2009

Ong 2010

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CONSTANT: Modelo mais recente para Trajetórias Semanticas

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