Semantic-based Trajectory Data Mining Methods

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

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

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

5. Motivada por um Modelo Conceitualpara Trajetórias

6. end (Professor, EPFL, 1988-2010) (Professor, Dijon, 1983-1988) (Lecturer, Paris VI, 1972-1983) (Assistant, Paris VI, 1966-1972) begin Trajetória Metafórica (Spaccapietra 2008) Time position institution

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) 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 Trajectory Stop Moveƒ(T) IsIn Place Modelo de Stops e Moves 0:N list Has 1:1 2:N list hasStops 1:1 0:1 From 1:1 1:1 To 0:1 0:N 0:N

10. Adicionando semântica às trajetórias: usando STOPS STOPS são dependentes da aplicação 1 2 Ibis Hotel [10:00-12:00]] Museu Louvre [13:00 – 17:00] Aeroporto [08:00 – 08:30] 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 trajectoryis a set of stops and moves • Stops have a place, a start time and an end time • Moves are characterized by two consecutive stops

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 stopC 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 applicationA 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]

15. 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 Jurere 09-12 IbisH. 13-14 FloripaS 16-17 (Alvares 2007ª) Semantic rich trajectories

16. Schema of Stops and Moves 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 Stops 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 Touristic Place Hotel 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,… 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 WHERE t.id='A' AND intersects (t.movingpoint.geomtetry,p.geometry) UNION … 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. Unknown stop Jurere 09-12 IbisH. 13-14 2.1: If intersects  during t stop  FloripaS 16-17 2.2: If no intersection during t unknown stop CB-SMoT: Speed-based clustering (Palma 2008) Input: Trajectory samples Speed variation minTime Output: stops and moves Step 1: find clusters Step 2: Add semantics to each cluster 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

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

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