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

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2 A importância de considerar a semântica T1 T2 T3 T4 T1 T2 T3 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

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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

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

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Motivada por um Modelo Conceitual para Trajetórias

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

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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 (t f - t i > 0) MOVE: parte da trajetória entre 2 stops consecutivos ou entre um stop e o início/fim da trajetória

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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

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

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1010 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] Congestionamento [09:00 – 09:15] Rótula [08:40 – 08:45] Aeroporto [08:00 – 08:30] Cruzamento [12:15 – 12:22] STOPS são dependentes da aplicação

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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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1212 Métodos para instanciar o modelo de stops e moves e minerar trajetórias semanticas

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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

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IB-SMoT (Alvares 2007a) A candidate stop C is a tuple (R C, C ), where R C 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 = {C 1 = (R C1, C1 ), …, C N = (R CN, CN )} of candidate stops with non-overlapping geometries R C1, …,R CN E.g. [Hotel - 3 hours, Museum – 1 hour] 4/12/ of 90

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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 FloripaS IbisH (Alvares 2007ª) 4/12/ of 90

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Schema of Stops and Moves Tid Mid S1id S2id geometry timest : : : : … :00 Tid Sid SFTname SFTid Sbegint Sendt 1 1 Hotel 1 08:25 08: TouristicPlace 3 09:05 09: TouristicPlace 3 10:01 14:20 Id Name Stars geometry 1 Ibis ,... 2 Meridien , … Id Name Type geometry 1 Notre Dame Church ,… 2 Eiffel Tower Monument ,… 3 Louvre Museum ,… Stops Moves Touristic Place Hotel Alvares (ACM-GIS 2007)

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

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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)

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

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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

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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 FloripaS IbisH Unknown stop 2.2: If no intersection during t unknown stop CB-SMoT: Speed-based clustering (Palma 2008) 4/12/ of 90 Tutorial on Spatial and Spatio-Temporal Data Mining (ICDM 2010)

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2 22 Stops (Methods SMot and CB-SMoT)

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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 4/12/ of 90

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2424 Resultados obtidos com os Métodos que Agregam Semântica – Trajetórias de Barcos de Pesca

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2525 Resultados obtidos com os Metodos que Agregam Semântica – Trajetórias de Barcos de Pesca

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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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2727 CONSTANT: Modelo mais recente para Trajetórias Semanticas (Bogorny et al. 2012)

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