1. Semantic-based Trajectory Data Mining Methods
Vania Bogorny
INE – UFSC

2. A importância de considerar a semântica
Cinema R
Restaurant SC T3 H
Hotel T2 T3 T2 T1 T4 T1 T4
Padrão Geométrico
Padrão SEMÂNTICO
Hotel p/ Restaurante, passando por SC
(b) Cinema, passando por SC

3. 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. Principal Problema: Falta de semântica
Dados Geográficos
Trajetórias Brutas (x,y,t)
Geografia + Trajetória Bruta =
Trajetória Semântica

5. Motivada por um Modelo Conceitual para Trajetórias

6. Trajetória Metafórica (Spaccapietra 2008)
end
Time
(Professor, EPFL, )
(Professor, Dijon, )
(Lecturer, Paris VI, )
(Assistant, Paris VI, )
position
institution
begin

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 7

8. The Model of Stops and Moves (Spaccapietra 2008)
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 8

9. Modelo de Stops e Moves Traveler location Has Trajectory hasStops From
0:N list
Has
1:1
Trajectory
2:N list
hasStops
1:1
0:1
From
1:1
Stop
Move ƒ(T)
1:1
0:1 To
0:N
IsIn
0:N
Place

10. Adicionando semântica às trajetórias: usando STOPS
STOPS são dependentes da aplicação 1 2
Ibis Hotel
[10:00-12:00]]
Aeroporto
[08:00 – 08:30]
Museu Louvre
[13:00 – 17:00]
Torre Eifel
[17:30 – 18:00] 3
Rótula
[08:40 – 08:45]
Congestionamento
[09:00 – 09:15]
Cruzamento
[12:15 – 12:22]
Aeroporto
[08:00 – 08:30]

11. 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 11

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

13. Methods to Compute Stops and Moves
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

14. 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]
4/11/2017

15. IB-SMoT Semantic rich trajectories
(Alvares 2007ª)
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
4/11/2017

16. Schema of Stops and Moves
Tid Sid SFTname SFTid Sbegint Sendt
Hotel : :40
TouristicPlace : :30
TouristicPlace : :20
Stops
Moves
Tid Mid S1id S2id geometry timest
:41 :
:04
: …
:00
Hotel
Touristic Place
Id Name Stars geometry
1 Ibis , ...
2 Meridien , …
Id Name Type geometry
1 Notre Dame Church ,…
2 Eiffel Tower Monument ,…
3 Louvre Museum ,…
Alvares (ACM-GIS 2007)

17. Queries: Trajectory Samples X Stops and Moves
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.timest
Trajectory samples
Semantic Trajectories
SELECT 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
Alvares (ACM-GIS 2007)

18. Queries: Trajectory Samples X Stops and Moves
Q1: Which are the places that moving object A has passed during his trajectory?
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
intersects (t.movingpoint.geomtetry,p.geometry) …
SELECT SFTname as place
FROM stop
WHERE id='A‘
Alvares (ACM-GIS 2007)

19. Queries: Trajectory Samples X Stops and Moves
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.timest
Trajectory samples
Semantic Trajectories
SELECT 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
Alvares (ACM-GIS 2007)

20. Queries: Trajectory Samples X Stops and Moves
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)

21. CB-SMoT: Speed-based clustering
(Palma 2008)
Input: Trajectory samples
Speed variation
minTime
Output: stops and moves
Step 1: find clusters
Unknown stop
2.2: If no intersection
during t  unknown stop
2.1: If intersects  during t  stop 
Jurere
09-12
FloripaS
16-17
IbisH.
13-14
Step 2: Add semantics to each cluster
4/11/2017
Tutorial on Spatial and Spatio-Temporal Data Mining (ICDM 2010)

22. Stops (Methods SMot and CB-SMoT)

23. 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
4/11/2017

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

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

26. 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

27. CONSTANT: Modelo mais recente para Trajetórias Semanticas (Bogorny et al. 2012)