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

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

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

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

Motivada por um Modelo Conceitual para Trajetórias

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

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

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

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

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]

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

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

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

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

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

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 Hotel Touristic Place 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)

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)

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)

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)

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)

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)

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 4/11/2017

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

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

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