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Trajetórias de objetos Trajetórias de objetos móveis: móveis: você já pensou você já pensou que pode estar sendo que pode estar sendo monitoriado ?monitoriado ?

Vania [email protected]

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Roteiro

O que são Trajetórias de Objetos Móveis?O que são Trajetórias de Objetos Móveis?

Para que servem Trajetórias?

Pesquisa em Trajetórias

Dados de trajetorias e problemas com estes dados

Bancos de Dados de Trajetórias

Modelagem de Trajetórias e a Importância dos Aspectos Semânticos

Mineração de Trajetórias

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A Explosão da Rede Sem Fio

Você utiliza algum desses dispositivos ?

Você alguma vez já se sentiu monitorado?

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A Explosão da Rede Sem Fio

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• Mundo está cada vez mais móvel....

Dispositivos móveis deixam traços digitais que podem ser coletados como trajetórias, descrevendo a mobilidade de seus usuários

Geram um novo tipo de dado, chamado “ Trajetorias de Objetos Moveis”

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Exemplos de Trajetórias de GPS: Barcos de Pesca de Atum

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Exemplos de Trajetórias de GPS: Barcos de Pesca

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Exemplos de Trajetórias de GPS: Veículos

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Exemplos de Trajetórias de GPS: Veículos

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Trajetórias Geradas por Telefone Celular

= célula (abrangência de uma antena de telefonia celular)

Mobility Data Analysis

Several analysis may be done over trajectories:

How people move around the town During the day, during the week, etc.

Are there typical movement behaviours? In a certain area at a certain time?

How are people movement habits changing in this area in last decade-year-month-day?

Are there relations between movements of two areas?

.....

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Serviços de Localização (Passado)

Limitados a sinais de tráfego fixos

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Serviços de Localização (Hoje)

TráfegoTráfego

Quantos carros estão na Estrada X?

Qual é o tempo estimado para chegar ao destino?

Busca baseada em localização:Busca baseada em localização:

Quais são os restaurantes no raio de 5KM da minha posição atual?

Onde está a churrascaria mais próxima?

AvisosAvisos::

Envie cupons a todos os clientes num raio de 4 KM da minha loja

Mobility Data Analysis: Applications

Trajectory data analysis may be useful in several application

domains

Veicule MonitoringVeicule Monitoring

Transportation Companies monitor their trucks

Insurance companies use GPS devices to monitor insured

vehicles to reduce insurance price

Traffic AnalysisTraffic Analysis

To alert people about traffic jams, accidents, etc...

Identify/predict low traffic regions in a city

SecuritySecurity

To identify a call


Animal Migration / Behaviour AnalysisAnimal Migration / Behaviour Analysis

Which are the trajectories of a given migration

bird?

Where do birds stop? For how long?

Which is the migration pattern of certain species?

Fishing Analysis and ControlFishing Analysis and Control

Are boats really fishing allowed areas?

Can we classify vessel trajectories?


Weather prediction and movement Weather prediction and movement

analysisanalysis

Hurricane trackingHurricane tracking

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

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Como é um dado de trajetória computacionalmente falando?

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Trajetórias brutas:

<(x1,y1,t1), (x2,y2,t2), (x3,y3,t3),... (xn,yn,tn)>

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Exemplo de uma tabela com trajetórias reais

TID X Y DATA HORAA 680271,8508 7462623,6403 07 09 04 20 59 28A 680272,0240 7462623,8229 07 09 04 20 59 29A 680271,8575 7462624,1940 07 09 04 20 59 30A 680271,5200 7462624,5672 07 09 04 20 59 31A 680271,0138 7462625,1270 07 09 04 20 59 32A 680270,0036 7462626,4312 07 09 04 20 59 34A 680269,6661 7462626,8044 07 09 04 20 59 35B 680269,6705 7462627,1735 07 09 04 15 59 36B 680269,6772 7462627,7272 07 09 04 16 05 37

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Trajectory Data

Spatio-temporal Data

Represented by a set of points located in space and time (time-stamped coordinates)

T=(t1,x1,y1), …, (tn, xn, yn) => position at time ti was (xi,yi)

Fosca Giannotti 2007 – www.geopkdd.eu

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Trajectories: Basic Concepts

Trajectories are represented by finite sequences of time-referenced locations, that result from various ways used to observe movements:

• time-based recording: positions of entities are recorded at regularly spaced

time moments, e.g. every 5 minutes;

• change-based recording: a record is made when the position of an entity

differs from the previous one;

• location-based recording: records are made when an entity comes close to

specific locations, e.g. where sensors are installed;

• event-based recording: positions and times are recorded when certain

events occur, in particular, activities performed by the moving entity (e.g.

calling by a mobile phone);

• various combinations of these basic approaches.

Typically, positions are measured with uncertainty. Sometimes it is possible

to refine the positions taking into account physical constraints, e.g. the

street network.

(Adrienko 2008)

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Trajectories: Basic Concepts

Movement-related characteristics include:

• time, i.e. position of this moment on the time scale;

• position of the entity in space;

• direction of the entity’s movement;

• speed of the movement (which is zero when the entity stays in the same

place);

• change of the direction (turn);

• change of the speed (acceleration);

• accumulated travel time and distance.

(Adrienko 2008)

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Trajectories: Overall Characteristics• geometric shape of the trajectory (fragment) in the space;

• travelled distance, i.e. the length of the trajectory (fragment) in space;

• duration of the trajectory (fragment) in time;

• mean, median, and maximal speed;

• dynamics (behaviour) of the speed:

– periods of constant speed, acceleration, deceleration;

– characteristics of these periods: start and end times, duration, initial and final positions, initial and final speeds, etc.;

– arrangement (order) of these periods in time;

(Adrienko 2008)

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Trajectories: Overall Characteristics

• dynamics (behaviour) of the directions:

– periods of straight, curvilinear, circular movement;

– characteristics of these periods: start and end times, initial and final positions and directions, major direction, angles of the curves, etc.;

– major turns (‘turning points’) with their characteristics: time, position, angle, initial and final directions, and speed of in the moment of the turn;

(Adrienko 2008)

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Relationships

Generally, the goal of comparison is to establish relations between the objects that are compared. Here are some examples of possible relations:

• equality or inequality;

• order (less or greater, earlier or later, etc.);

• distance (in space, in time, or on any numeric scale);

• topological relations (inclusion, overlapping, crossing, touching, etc.).

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Relationships

Many other types of relations may be of interest, depending on the problem in hand:

• similarity or difference of the overall characteristics of the trajectories (i.e. shapes, travelled distances, durations, dynamics of speed and directions, and so on);

• spatial and temporal relations:

– co-location in space, full or partial (i.e. the trajectories consist of the same positions or have some positions in common):

· ordered co-location: the common positions are attained in the same order;

· unordered co-location: the common positions are attained in different orders;

– co-existence in time, full or partial (i.e. the trajectories are made during the same time period or the periods overlap);

– co-incidence in space and time, full or partial (i.e. same positions are attained at the same time);

–

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Relationships

Possibly-Sometime-Inside Possibly-Always-Inside Always-Possibly-Inside

(Wolfson 2004)

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Relationships

Definitely–Always–Inside Definitely–Sometime–Inside Sometime–Definitely–Inside

(Wolfson 2004)

Raw Trajectory Data: Problems and Solutions

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The trajectory reconstruction problem

From raw location data (tid, x, y, t)

To trajectory data (obj-id, traj-id, (x, y, t)+)

a sample of a user’s

movement (GPS

recordings)

a sample of

reconstructed

trajectories

(Theodoridis and Peleikis 2007)

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Trajectory stream manager

Trajectory stream manager operations

receives raw location data about mobile users’ movement

reconstructs trajectories (excluding noise, etc.) and posts trajectory data to a MOD (Moving Object Database)

Results so far – 2 alternatives

Assumptions about trajectory ‘birth’ (for spatial/temporal gaps between traces)

Studying the notion of ‘stop’ (suspension of an entity’s movement)


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Trajectory stream manager (now…) (1)

When will an object have assigned a new trajectory-id?

When there is sufficiently large gap in the spatial dimension between two consecutive recorded positions

When there is sufficiently large gap in the temporal dimension between two consecutive recorded positions

x

t

t0=09:00

tn=10:00

tn+1=10:30

tm=11:00


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Trajectory stream manager (now…) (2)

Dealing with noise

GPS-sampled positions may include noise, which should be excluded from trajectory reconstruction

A naïve approach computes the speed of the object in each segment of its motion and compares it with a commonly accepted maximum speed vmax (e.g. 200 km/h for cars)

In such a case, the stream manager rejects the last (marked as noisy) position and waits for the next (perhaps, acceptable) position to reconstruct a new segment

Vi>Vmax

Vi>Vmax


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BANCOS DE DADOS DE OBJETOS MOVEIS

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O poder de BD de Objetos Móveis (Wolfson 1999)

MOD

Restrição: Aeronaves devem voar a uma distância mínima de 2km entre si.

Futuro: Quais caminhões chegarão ao seu destino nos próximos 20

minutos?PresentePresente: Onde estão os táxis a menos de 1 KM de onde estou?: Onde estão os táxis a menos de 1 KM de onde estou?

Passado:

Durante o ultimo ano, quantas vezes o ônibus 435 atrasou mais de 10 minutos ao passar pela parada 215?

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Protótipos de Bancos de Dados de Objetos Móveis

SECONDO – Ralph Guting (Alemanha)

HERMES – Yannis Theodoridis and Nikos Pelekis (Grécia)

Secondo

University of Hagen

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Data Types (Guting 1999)

Data Types: mpoint e mregion são mapeamentos do tempo para o espaço mpoint = ponto no tempo

mregion = região no tempo

Exemplos: vôo (id: string, origem: string, destino: string, rota: mpoint)

tempestade (id: string, tipo: string, area: mregion)

Moving Point (mpoint)

Moving Region (mregion)

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Operadores Espaço-Temporais

Exemplos de Operadores:

Intersection (mpoint, mregion) → mpoint

distance (mpoint, mpoint) → mreal

trajectory (mpoint) → line

deftime(mpoint) → period

length (line) → real

(Guting 1999)

t1t2 t3

t4

t5

t2 t3t4 t5t1

t0 tn

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Consultas Espaço-Temporais

vôo (id: string, origem: string, destino: string, rota: mpoint)

Consulta 1: “Encontre os vôos de São Paulo que voaram mais de 4000 km.”

SELECT *

FROM voo

WHERE origem = ’SP’ AND length (trajectory (rota) ) > 4000

Consulta 2: “Encontre os pares de aviões que durante seus vôos se aproximaram em menos de 2000 metros!”

SELECT f.id, g.id

FROM voo f, voo g

WHERE f.id <> g.id AND min (distance (f.rota, g.rota) ) < 2000

t1t2 t3

t4

t2 t3t4 t5t1

Hermes

University of Pireaus

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Hermes

Dimensão espacial e temporal (tipo de dado PONTO)

HERMES Moving Data Cartridge (MDC)

Implementado como um novo módulo, similar ao Oracle Spatial Data Cartridge

Implementa diversos operadores espaco-temporais para relacionamentos espaço-temporais e similaridade:

Trajetórias individuais

Grupos de trajetórias


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Operações

Gera um poligono ao redor um timestamp

f_buffer

Calcula a distância entre dois pontos (tempo) de 2 objetos móveis

f_distance

Verifica se um objeto está a frente de um ponto em um certo instante de tempo

f_front

Verifica se um objeto está a atrás de um ponto em um certo instante de tempo

f_behind

.....rico grupo de operações espaciais

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HERMES (Arquitetura)(Theodoridis and Peleikis 2007)

TemporalDimension

SpatialDimensionHermes

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Projetos na Area de Trajetorias

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Requisitos da Aplicação

Visualização dos Dados e Padrões

Privacidade

Data Warehouse

e SGBD

Fornecimento

De

Dados

Mineração de Dados

Modelagem Conceitual e

Ontologias

GeoPKDD – O PRIMEIRO projeto europeu na área de ANALISE trajetórias (2006 – 2009)

Teoria de BD

Espaço-temporais

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MODAP– O SEGUNDO projeto europeu na área de trajetórias (2010 – 2012)

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SEEK– PROJETO BRASIL – EUROPA (2012 – 2014)

Universidades BRA: UFSC, PUC-Rio, UFSC, UFPEUniversidades Europeias: Italia (UniPisa, UniVeneza),

Pireaus (Grecia)

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