Mobility Data Management - Aalborg...

1

Mobility Data Management

Yannis Theodoridis & Nikos Pelekis

InfoLab, University of Piraeus, Greece infolab.cs.unipi.gr

Aalborg, September 2011

Mobility Data Management

Yannis Theodoridis & Nikos Pelekis

InfoLab, University of Piraeus, Greece infolab.cs.unipi.gr

Aalborg, September 2011

2

3

Acknowledgments

� The content of this tutorial has been inspired by partners’ work in

the following EU projects:

� FP6/GeoPKDD (http://www.geopkdd.eu), 2005-09

� FP7/MODAP (http://www.modap.org), 2009-12

� ESF/COST-MOVE (http://move-cost.info), 2009-13

� Also, special thanks to our colleagues at InfoLab, U. Piraeus

GeoPKDDGeographic Privacy-awareKnowl. Discovery & Delivery

4

Log file

� Previous versions of this material:

� 2011

� Mobility Data Management. Post-graduate course @ KAUST, Jeddah, Saudi

Arabia, June 2011.

� From Mobility Data Management to Location-based Services. PhD short

course @ Univ. Milano, Italy, May 2011.

� Recent Advances in Mobility Data Mining. Invited talk @ Univ. Zurich,

Switzerland, Apr. 2011.

� 2010

� Mobility Data Management: theory and practice with Hermes. Hands-on

seminar @ MODAP summer school, Rhodes, Greece, Aug. 2010.

� From Mobility Data Management to Location-based Services. PhD short

course @ Univ. Venice, Italy, Jun. 2010.

3

5

Introduction, Overview

From digital mapping to mobile social networking –

A tour on geospatial information management

challenges

6

Mobile devices and services

� Large diffusion of mobile devices, mobile services and location-based

services � mobility data

4

7

Which mobility data?

� Location data from mobile phones

� i.e., cell positions in the GSM/UMTS network

� Location data from GPS-equipped devices

� Humans (pedestrians, drivers) with GPS-equipped smartphones

� Vessels with AIS transmitters (due to maritime regulations )

� Location data from intelligent transportation environments

� Vehicular ad-hoc networks (VANET)

� Location data from indoor posistioning systems

� RFIDs (radio-frequency ids)

� Wi-Fi access points

� Bluetooth sensors

8

Examples of mobility data

� Vehicles moving in Milan

� ~2M GPS recordings from 17241 distinct

objects (7 days period) � 214,780

trajectories

� Vessels sailing in Mediterranean

sea

� (only a small subset of the dataset at

hand) ~4.5M GPS recordings from 1753

distinct objects (3 days period) � 1503

trajectories

5

9

What can we learn from mobility

data ...

10

Vehicles datasets…

� (global) Traffic monitoring

� How many cars are in the ring of the town?

� Once an accident is discovered, immediately send

alarm to the nearest police and ambulance cars

� (personalized) Location-aware queries

� Where is my nearest Gas station?

� What are the fast food restaurants within 3 miles from

my location?

� Let me know if I am near to a restaurant while any of

my friends are there

� Get me the list of all customers that I am considered

their nearest restaurant

6

11

Vessels datasets…

(requirements from Greek Maritime Conservation Agencies)

� For each ship and for each day

� Extract / draw the ship tracks (detailed vs. simplified)

� Calculate average and minimum distance from shore; where and when

� Calculate the maximum number of ships in the vicinity of the ship (e.g. 10 n.m.

radius)

� Find whether (and how many times) a ship goes through narrow passages or

biodiversity boxes

� Calculate the number of sharp changes in direction

� For the full population of ships

� Find typical routes vs. outliers (full or small part of its track)

12

More ambitious: “Mobile Landscapes”[Ratti et al. 2005]

MIT senseable project: http://senseable.mit.edu/grazrealtime/

7

13

More ambitious: “Trajectory patterns”[Giannotti et al. 2007]

∆∆∆∆T ∈∈∈∈ [10min, 20min]

∆∆∆∆T ∈∈∈∈ [20min, 35min]

∆∆∆∆T ∈∈∈∈ [5min, 10min]∆∆∆∆T ∈∈∈∈ [25min, 45min]

EU GeoPKDD project: http://www.geopkdd.eu

14

The big picture

8

15

Mobility Data

Raw data

Mobility Patterns

GSM network, WSN, GPS

End user

Mobility manager

16

Multimedia & Geo

GSM, GPS

Mobility models

Mobility Database

Mobility expert

9

17

Multimedia & Geo

GSM, GPS

Mobility models

End user

Mobility Database

18

The modules of our architecture

raw data producers

Lat/Lon to x/y

data conversion Trajectory

reconstructionTrajectory

aggregations

(DW)

Lat/Lon

locations

(DB)

x/y

locations

(DB)

Mobility Data

VisualizationMobility Data

Querying

Mobility Data

MiningTrajectories

(MOD)

Mobility Data

OLAP

Extract-

Transform-

Load (ETL)

10

19

Key questions that arise

� How to reconstruct a trajectory from raw logs?

� How to store trajectories in a DBMS?

� Is a trajectory simply a sequence of (x, y, t) tuples?

� What kind of analysis is suitable for mobility data?

� In particular, trajectories of moving objects?

� How does infrastructure (e.g. road network) affect this analysis?

� Which patterns / models can be extracted out of them?

� Clusters, frequent patterns, anomalies / outliers, etc.

� How to compute such patterns / models efficiently?

� How to protect privacy / anonymity?

� trade-off between privacy protection and quality of analysis

20

A guided tour on Mobility Data Mgmt

I. Mobility data storage and querying

Acquiring trajectories from raw data; Location-aware querying; Efficient trajectory indexing and storage in MODs

II. Mobility-aware applications and tools

Location-based services and tools; Algorithms and operations for LBS

III. Mobility data analysis and mining

Trajectory warehousing and OLAP; Mobility data mining and reasoning; Visual analytics for mobility data

IV. Privacy aspects

Preserving user traces’ anonymity

V. Outlook

Open issues – Future Challenges

11

21

A pot-pourri of what we ‘ll see in

the next lectures ...

22








IV. Privacy aspects


V. Outlook


12

23

Part I. Mobility data storage and querying

� Basic characteristics of moving

objects

� Large number of objects

� Frequent location updates

� Fast query processing

� Diversity in space / time

� Issues:

� Trajectory reconstruction

techniques (from raw

locations to trajectories)

� Simple vs. advanced queries

involving space / time

� Robust MOD engines for

efficient trajectory data

storage, indexing, and query

processing

24

The trajectory reconstruction problem

� From raw data, i.e., time-stamped locations

� Raw data (3D points) arrive either one-by-one or in bulks

� … to trajectory data, i.e., continuous evolutions

� Redundancy is reduced, noise is removed, etc.

13

• Semantic Trajectory: T={efirst,…,elast}

• Episode: ei=(tfrom, tto, place, tag)

raw mobility data

sequence (x,y,t) points

e.g., GPS feeds

Home (breakfast)office (work) Market (shopping)Home (relax)

Road

(bus)

Train

(metro)

Sideway

(walk)

[~, 8am]

[8am, 9am] [6pm, 6:30am] [7:30pm, 8pm]

[9am, 6pm] [6:30pm, 7:30pm] [8pm,~]

meaningful mobility tuples

<place, timein, timeout, tags>

What is semantic trajectory?

25

26

Example mobility-aware queriesoriginal slides from [Mokbel & Aref, 2007]

� (I’m a driver) Where will my nearest McDonald’s

be for the next hour?

� (I’m a gas station) Send E-coupons to all cars that

I am their nearest gas station

� (I’m a traffic manager) Continuously report the

number of cars in the freeway

� (I’m taxi A driver’s colleague) What was the

closest dist. between Taxi A & me yesterday?

14

27

Indexing, Query Processing, etc.

� Query mechanism

� What happens inside the DBMS?

� Efficient storage of mobility data

� Indexing, query processing & optimization techniques

Data

Query

Answer

28








IV. Privacy aspects


V. Outlook


15

29

Part II. Mobility-aware applications

� Q: Apart from scientists and engineers, who can take advantage of

the above?

� A: Everybody!

� How? Location-based services (LBS)

source: iphone-2.co.uk

30

LBS is easy to get

� High-end smartphones with build-in GPS receiver

� Nokia N8, N97, …

� Sony Ericsson Xperia X10, P1i, …

� HTC HD2, P3600 i-go, …

� Apple iPhone

� Google’s Android

� etc.

� etc.

source: iphone-2.co.uk

16

31

LBS vendors: from Google to Apple

� See in real time where your friends

are! (launched Feb.09 by Google)

� “Find my iPhone”

� Track your lost iPhone

(launched Jun.09 by Apple)

source: apple.com

source: google.com

32

From naïve to advanced LBS

� From Routing (“get directions”), What-is-

around (“search nearby”) and Find-the-

nearest

� http://g.co/maps/38yfy

� … to “Zombies, Run!” online adventure game

� http://www.zombiesrungame.com/

17

33








IV. Privacy aspects


V. Outlook


34

Part III. Mobility data analysis and mining

� OK, trajectories are stored in MOD engines

� Efficiently updated, queried

� What about analyzing data and discovering knowledge?

� Trajectory data warehousing for OLAP analysis

� Trajectory data mining for knowledge discovery

� What kind of patterns can be extracted from trajectories (clusters?

sequences?)

� Trajectory data/pattern visualization for better interpretation of

OLAP/KDD results

18

35

Aggregations on mobility data

36

Frequent pattern mining

� T-patterns

19

37

Trajectory clustering

� Finding clusters of trajectories and outliers

� What about clusters’ centroids?

38

Trajectory sampling

� Can we get the gist of a real large MOD by visualizing it? Can we do

this automatically?

� If yes, we can

� extrapolate the query results from queries in the sampled MOD

� discover mobility patterns working with a “representative” subset

20

39








IV. Privacy aspects


V. Outlook


40

Part IV. Privacy aspects

“New technologies can pinpoint your location at any time and place. They

promise safety and convenience but threaten privacy and security”

Cover story, IEEE Spectrum, July 2003

YOU ARE

TRACKED …

!!!!

21

41

Linkage in Mobility Data

� By intersecting the phone directories of locations A and B we find that

only one individual lives in A and works in B.

� id:34567 � Prof. Smith

� Then you discover that on Saturday night id:34567 usually drives to

the city red lights district. Too bad for Prof. Smith �

A

A

B

B

[almost every day Mon-Fri between 7:45 – 8:15]

[almost every day Mon-Fri between 17:45 – 18:15]

id:

34567

42








IV. Privacy aspects


V. Outlook


22

43

Part V. Outlook

� What’s next? Mobile social networks

� Facebook, Twitter, etc.: currently, 1 billion users of social media; what

if their movement is added?

� Towards complex social networks of moving interacting objects.

Leadership

Path following

Goal seek

44

Before we start this tour ...

A quick survey on background

knowledge

23

45

Outline

� Spatial database management

� Spatial data models and Spatial DBMS extensions

� Spatial indexing and query processing

� Infrastructure for Location-based Services

� Positioning technologies – GPS etc.

46

Spatial data modeling

24

47

Geographical data models

� Representation of reality:

Raster vs. Vector

x

y

0 400 800

400

800

street

river

house

house

Trees II

Trees I

bridge

II

I I

II

II II

h

II hb

r

r

r r

r r

r

r

r

r

s

s s

s

s s

s

7

6

5

4

3

2

1

0

0 1 2 3 4 5 6 7

reality

raster vector

48

Vector model

� Geographical space = a set of entities

� 0-d: points

� 1-d: line segments, polylines, …

� 2-d: polygons, polygons with holes, …

edge

vertex

end

end

Open line Closed line

Simple

polygon

Non-simple

polygon

Convex

polygon

Polygon

with hole(s)

25

49

Spatial relationships

� Topological vs. directional relationships between spatial objects

� Topological relationships are invariant to topological transformations

� Shift, Rotation, Scaling

50

Topological relationships

� Egenhofer’s 4- and 9-

intersection model

(Egenhofer and

colleagues, 1989-93)

� Based on the set

intersections between

objects’ interior,

boundary and exterior

disjoint

meet

overlap

inside

contain

covered-by

cover

equal

26

51

Egenhofer’s Nine-Intersection Model

0 0 1

0 0 1

1 1 1

1 1 1

0 0 1

1 1 1

1 1 1

1 1 1

�� disjoint

0 0 1

0

0

1 1 1

0

0

1

0 0 1

0 1

1 1 1

0

1

0

1 1 1

contains inside equal

meet covers coveredBy overlap

1

1 0

0 0

0

0 0

1

1

1

1

0

1

1

1

1

0

1

1 1 0

0

52

Temporal relations

� Allen’s interval

algebra (Allen, 1983)

� A set of 13 relations

between intervals A,

B

Relation Definition

t

AA B

AB

t

A B

t

A B

t

B

A

tAB

t

AB

t

A takes place before B(and its dual)

A is equal to B

A meets B(and its dual)

A during B(and its dual)

A overlaps with B(and its dual)

A finishes B(and its dual)

A starts B(and its dual)

27

53

Example of a geo-DB

� Entities: countries, cities, rivers, etc.

� Relationships between entities: capital-of-country, …

� How can we model and manage such information?

� Country is of polygon shape

� River is of polyline shape (?)

� City is of point shape (?)

� Spatial operations:

� area of a country, length of borderline between 2 countries, ...

Map source: geographyjim.org

54

Example: World Database

� At the conceptual level

� 3 Entities: Country, City, River

� 2 Relationships: capital-of, originates-in

POPULATION

CAPITAL

ORIGINATESCOUNTRY RIVERCITY

NAME

CAPITAL-OF

NAME

POPULATION

LIFE-EXP

NAMELENGTH

CONTINENT

GDP

28

55

Example: World Database (cont.)

� At the logical level: 3 relations (Country, City, River)

56

How do we implement spatial DBs

� An example Spatial DBMS:

PostgreSQL

� Geometric data types

� Point

� Line segment = 2 points

� Box = 2 points

� Path = sequence of points

� Polygon = sequence of

points

� Circle = point + number

(radius)

� Spatial indexing techniques

� GiST – generalized search

trees

� Special case: R-tree

� Geometric functions and

operators

� A variety …

29

57

PostgreSQL geometric operators

Operator Description Example

58

PostgreSQL geometric operators (cont.)

Operator Description Example

30

59

PostgreSQL DDL

� A table of areas (zones)

CREATE TABLE zones ( poly_id integer,name varchar(30),sector polygon);

INSERT INTO network.zones VALUES (1, ’PARK’, ’(479243, 4204000, 477728, 4202750,477559, 4202100, 476271, 4204750)’ :: polygon);

� A table of points (locations)

CREATE TABLE locations ( point_id integer, name varchar(30), pos point);

INSERT INTO locations VALUES (52, ‘Freedom Sq.’, ‘(476600, 4202800)’ :: point);

60

PostgreSQL DML

� Objects within distance from a given point

SELECT point_id,(pos<->Point '(475750, 4201500)') as distance

FROM locations

WHERE (pos <-> Point '(475750, 4201500)' ) <= 200

� Objects within a given region

SELECT point_id, name

FROM locations

WHERE (pos @ polygon '(479243, 4204000, 476271, 4204750)') = TRUE

SELECT point_id, name

FROM locations

WHERE (pos @ (SELECT sector FROM zones WHERE name = 'PARK'))

31

61

SDBMS physical level

� Issue:

� How to store and efficiently process this kind of information?

� Relational DBMS support traditional (alphanumeric, etc.) data types

� Low complexity � relational tables are efficient

� Total ordering � search trees (e.g. B+-trees) for fast search

� Unfortunately, spatial objects

� (a) are of high complexity and

� (b) lack total ordering

x

y

0 400 800

400

800

street

river

house

house

Trees II

Trees I

bridge

62

SDBMS physical level

� Unfortunately, spatial objects

� (a) are of high complexity and (b) lack total ordering

� Nevertheless, can we do something?

� Regarding (a): use spatial data approximations of low complexity

� Regarding (b): adopt multi-dimensional search techniques

France

32

63

Spatial data approximations

� Minimum (Orthogonal) Bounding Rectangle (MBR)

� MBR(obj) is the minimum orthogonal rectangle that covers obj

� Unfortunately, approximations are not identical to the original

shapes they origin from �

� Need for a filter and refinement procedure to support typical spatial

queries

France

64

(parenthesis: “typical spatial queries”)

� Typical spatial queries are the

following:

� Point (D, p): find objects in dataset

D covering point p

� Range (D, p): find objects in dataset

D that lie inside (or overlapping)

region r

� NN (D, p, k): find the (k-) object(s)

in dataset D that lie nearest to

point p

� SpatialJoin (D1, D2): find all pairs

(o1,o2) of objects in datasets D1,

D2, that satisfy a spatial condition

(usually, overlap)

Map source: geographyjim.org

33

65

The Filter-Refinement procedure

� Processing a spatial query Q

� Filter step: find a set S that contains (for sure) the answer set of Q

� Using MBR approximations

� Refinement step: find the exact answer set of Q

� Geometrically processing S

Query

Spatial Index

Candidate Set

Filter Step

False Hits

Refinement Step

Local Object Geometry

Test on exact Geometry

Hits

Query Result

66

An example of filter – refinement

� Range query processing

� filter step: find object MBRs overlapping Q

� refinement step: find objects overlapping Q

B

C D

A

B

C D

Query

region

MBR

Data

Object B

C

34

67

Indexing spatial objects

� Problem:

� Cannot adopt “total ordering” in multi-dimensional space

� Solutions:

� Adopt partial ordering (using space filling curves, e.g. Hilbert), transform 2D objects in 1D intervals, and exploit on e.g. B+-trees, or

� Invent novel spatial indexing techniques: R-trees, Quadtrees, etc.

� The latter solution turned out to be the winner.

Hilbert curve

68

Space filling curves

Row Row-prime Z-Order (Morton)

Hilbert Cantor diagonal Spiral

35

69

Z- vs. Hilbert curve

� Z-curve:

� Produced by interleaving bits

x 5 0 0 0 0 00 (2,4)

5 (24)

1 1

0 0 0 1 1 00 0

y 5

n=3n=2n=1n=0

70

Z- vs. Hilbert curve

� Hilbert curve:

� Produced by interleaving bits

� … and rotating

n=3n=2n=1n=0

A

A A

A

36

71

R-tree (and family)

� The basic idea: extends B-tree to multi-dimensional space

� Basic properties:

� Nodes correspond to disk pages; Balanced tree; Nodes consist of MBRs covering the entries of the lower level

� Implemented in Oracle, IBM DB2, PostgreSQL, etc.

� Finds many applications: spatial, image, multimedia, time-series databases, OLAP, etc. (Manolopoulos et al. 2005)

72

Point / Range query processing in R-trees[Guttman, 1984]

� Query window: Q

� Root level: Q overlaps R1, R2

� Depth-first propagation: node R1 � node R3, node R2 � node R5 � overlaps r7

� Answer set: r7

Q

37

73

NN query processing in R-trees[Roussopoulos et al. 1995]

� Query point: Q

� Root level: R1, R2 are candidates (at the moment…) for containing the answer

� Depth-first propagation (ask yourselves why…): node R1 � node R3 � r2 and r3

are candidate answers

� side-effect: R2 is pruned!

� Answer set: r2, r3 (two candidates for the 1-NN !!)

Q

MINDIST

MAXDIST

MAXDIST

74

Spatial DB + Time = Spatio-temporal DB

� Including time dimension in spatial data is not straightforward(Koubarakis et al. 2003)

� time is not simply a 3rd dimension (monotonicity, etc.)

� Adding motion in spatial objects (points, lines, regions)

� Novel data types, e.g. “moving points” (Güting et al. 2000)

� Spatio-temporal extensions of R-trees for indexing (Theodoridis et al.

1998)

38

75

An example of spatio-temporal DB

� Vessel traffic:

� Entities: vessels, ports, coastlines, narrow passages, etc.

� Relationships between entities: vessels’ scheduled trips (port-to-port)

� Spatial operations:

when does a

vessel approach

(pass through)

a port (narrow

passage), how

close (and when)

do two vessels

approach each

other,...

Live information: vesseltracker.com

76

How do mobility data look like?

GPS recordings

39

77

GPS Data

� Raw data: GPS recordings

N; Time; Lat; Lon; Height; Course; Speed; PDOP; State; NSat

…

8;22/03/07 08:51:52;50.777132;7.205580; 67.6;345.4;21.817;3.8;1808;4

9;22/03/07 08:51:56;50.777352;7.205435; 68.4;35.6;14.223;3.8;1808;4

10;22/03/07 08:51:59;50.777415;7.205543; 68.3;112.7;25.298;3.8;1808;4

11;22/03/07 08:52:03;50.777317;7.205877; 68.8;119.8;32.447;3.8;1808;4

12;22/03/07 08:52:06;50.777185;7.206202; 68.1;124.1;30.058;3.8;1808;4

13;22/03/07 08:52:09;50.777057;7.206522; 67.9;117.7;34.003;3.8;1808;4

14;22/03/07 08:52:12;50.776925;7.206858; 66.9;117.5;37.151;3.8;1808;4

15;22/03/07 08:52:15;50.776813;7.207263; 67.0;99.2;39.188;3.8;1808;4

16;22/03/07 08:52:18;50.776780;7.207745; 68.8;90.6;41.170;3.8;1808;4

17;22/03/07 08:52:21;50.776803;7.208262; 71.1;82.0;35.058;3.8;1808;4

18;22/03/07 08:52:24;50.776832;7.208682; 68.6;117.1;11.371;3.8;1808;4

…

78

GPS Data

� Typical structure:

N;Time;Lat;Lon;Height;Course;Speed;PDOP;State;NSat

…

8;22/03/07 08:51:52;50.777132;7.205580; 67.6;345.4;21.817;3.8;1808;4

9;22/03/07 08:51:56;50.777352;7.205435; 68.4;35.6;14.223;3.8;1808;4

10;22/03/07 08:51:59;50.777415;7.205543; 68.3;112.7;25.298;3.8;1808;4

11;22/03/07 08:52:03;50.777317;7.205877; 68.8;119.8;32.447;3.8;1808;4

12;22/03/07 08:52:06;50.777185;7.206202; 68.1;124.1;30.058;3.8;1808;4

13;22/03/07 08:52:09;50.777057;7.206522; 67.9;117.7;34.003;3.8;1808;4

14;22/03/07 08:52:12;50.776925;7.206858; 66.9;117.5;37.151;3.8;1808;4

15;22/03/07 08:52:15;50.776813;7.207263; 67.0;99.2;39.188;3.8;1808;4

16;22/03/07 08:52:18;50.776780;7.207745; 68.8;90.6;41.170;3.8;1808;4

17;22/03/07 08:52:21;50.776803;7.208262; 71.1;82.0;35.058;3.8;1808;4

18;22/03/07 08:52:24;50.776832;7.208682; 68.6;117.1;11.371;3.8;1808;4

…

40

79

GPS Data

� 50.777317;7.205877 � ???

80

GPS Data

� (and the inverse) Aalborg Univ. � 57° 00′ 59.76″ N, 9° 58′ 43.15″ E

� Google maps link: Aalborg University

41

81

How do we collect mobility data?

Positioning infrastructure

82

Geo-positioning

� Positioning technologies (all standardized in early 2000’s)

� Using the mobile telephone network

� Time of Arrival (TOA), UpLink TOA (UL-TOA)

� Using information from satellites

� Global Positioning System (GPS)

� Assisted (A-GPS), Differential GPS (D-GPS)

source: ESA

source: http://www.3gpp.org

U-TDOA

hyperboloid

Antenna

Antenna

Antenna

Estimated

Location Region

RTT

Circle

42

83

Geo-positioning (cont.)

� Uplink - Time Difference of Arrival (U-TDOA)

� At least 3 receivers (located together with antennas) get signals from a

user’s mobile, triangulate, and

estimate its position

� Accuracy: 30-120 m

� Standardized by the 3GPP

(3rd Generation Partnership Project)

� Problem: Requires great

investment in infrastructure

source: 3gpp.org

U-TDOA

hyperboloid

Antenna

Antenna

Antenna

Estimated

Location

Region

RTT Circle

84

Satellite-supported positioning

� GPS (Global Positioning System)

� Initiated 1978 by US DoD; error-free for civilian applications since 2000

� A constellation of 24 satellites

� monitored by 5 monitoring stations and 4 ground antennas; handled with (extremely precise) atomic clocks

� At least 5 satellites are in view from every point on the globe

� GPS receiver gathers information from 4 (or 3, the minimum) satellites and (a) triangulates to position itself; (b) fixes its (non-atomic) clock

� Position accuracy: ~20m

43

85

source: (Swedberg, 1999)


� Assisted GPS (A-GPS)

� provides pre-

calculated satellite

orbits to the receiver

� Accuracy 10-20 m

� Differential GPS (D-GPS)

� accuracy down to 1m

86


� EGNOS/Galileo

� EU’s rival to US’ GPS

� constellation of 30 satellites; to be in operation by 2010 2013

� 1st Galileo satellite launched in Dec. 2005

� designed for civil use

� GPS compatible

� 1m accuracy

44

87

Conclusions

� Assumptions

� Wireless networks infrastructures are the nerves of our territory

� besides offering their services, they gather highly informative traces

about human (animal, etc.) mobile activities

� Ubiquitous computing infrastructure will further push this

phenomenon

� Therefore,

� Mobility data collections will be more and more popular …

� … asking for effective and efficient management, analysis, and

exploitation

88

Questions

45

89

Reading list

� Project overviews, Manifesto papers, etc.

� Atzori, P. (2007) Privacy and anonymity in location and movement-aware data analysis – the GeoPKDD approach. Proceedings of ISI.

� Giannotti, F. and Pedreschi, D. (2008) Mobility, Data Mining, and Privacy: A Vision of Convergence. In Mobility, Data Mining and Privacy – Geographic Knowledge Discovery. Springer.

� Giannotti, F. et al. (2008) Mobility, Data Mining, and Privacy – the Experience of the GeoPKDD Project. Proceedings of PinKDD.

� Lopez, X. (2003) The Future of GIS: Real-time, Mission Critical, Location Services. Proceedings of Cambridge Conference.

� Nabian, N. et al. (2009) MIT GEOblog: A Platform for Digital Annotation of Space and Collective Community Based Digital Story Telling. Proceedings of IEEE-DEST.

� Ratti, C. et al. (2005) Mobile Landscapes: Graz in Real Time. Proceedings of LBS & TeleCartogrqaphy.

90

Reading list

� Spatial & spatio-temporal data modeling

� Allen JF (1983) Maintaining knowledge about temporal intervals.Communications of the ACM, 11, 832-843.

� Egenhofer MJ (1989) A Formal Definition of Binary Topological Relationships.Proceedings of FODO Conference.

� Egenhofer MJ, Sharma J (1993) Topological Relations Between Regions in R²and Z². Proceedings of SSD Conference.

46

91

Reading list

� Spatial & spatio-temporal database management

� Güting, R.H. et al. (2000) A Foundation for Representing and Querying MovingObjects. ACM Transactions on Database Systems, 25(1):1-42

� Guttman, A. (1984) R-trees: A Dynamic Index Structure for Spatial Searching.Proceedings of ACM SIGMOD Conference.

� Koubarakis, M. et al. (2003) Spatio-Temporal Databases – the Chorochronosapproach. Springer.

� Manolopoulos, Y. et al. (2005) R-trees: Theory and Applications. Springer.

� Roussopoulos, N. et al. (1995) Nearest Neighbor Queries. Proceedings of ACM SIGMOD.

� Theodoridis, Y. et al. (1998) Specifications for Efficient Indexing in Spatio-temporal Databases. Proceedings of SSDBM.

92

Reading list

� Positioning and tracking technologies

� Bajaj R. et al. (2002) GPS: Location-Tracking Technology. IEEE Computer, 35(4):92-94.

� Bar-Noy, A. and I. Kessler (1993) Tracking Mobile Users in Wireless Communication Networks. IEEE/ACM Transactions on Information Theory, 39(6):1877-1886.

� Bulusu, N. et al. (2000) GPS-less Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications Magazine, 7(5):28-34.

� Djunkic, G.M. and R.E. Richton (2001) Geolocation and Assisted GPS. IEEE Computer, 34(2):123-125.

� Hofman-Wellenhoff, B. et al. (1997) Global Positioning System: Theory and Practice, 4th ed. Springer.

� Kaplan, E. (1996) Understanding GPS Principles and Applications. Artech House.

47

93

Reading list

� Positioning and tracking technologies (cont.)

� Mauve, M. et al. (2001) A survey on position-based routing in mobile ad hoc

networks. IEEE Network Magazine, 15(6):0-39.

� Misra, A. et al. (2004) An Information-Theoretic Framework for Optimal

Location Tracking in Multi-System 4G Networks. Proceedings of IEEE INFOCOM

Conf.

� Porcino, D. (2001) Location of Third Generation Mobile Devices: A Comparison

between Terrestrial and Satellite Positioning Systems. Proceedings of IEEE

Vehicular Technology Conf.

� Ward, A. et al. (1997) A New Location Technique for the Active Office. IEEE

Personal Communications, 4(5):42-47.

� Xiang, Z. et al. (2004) A Wireless LAN-based Indoor Positioning Technology.

IBM Journal of Research and Development, 48(5/6):617-626.

94

Reading list

� Indoor positioning and data management issues

� Jensen, C. S., Lu, H., Yang, B. (2009) Indexing the Trajectories of Moving

Objects in Symbolic Indoor Space. Proceedings of SSTD.

� Yan, B., Lu, H., Jensen, C. S. (2009) Scalable continuous range monitoring of

moving objects in symbolic indoor space. Proceedings of CIKM.

� Jensen, C. S., Lu, H., Yang, B. (2010) Indoor - A New Data Management

Frontier. IEEE Data Eng. Bull. 33(2): 12-17.

48

95

Online Resources

� Spatial – temporal database management

� ChoroChronos.org. A portal of datasets and algorithms for mobility data management. http://www.chorochronos.org

� R-tree-portal. A repository of R-tree related material. http://www.rtreeportal.org

� TimeCenter – an international center for temporal database applications. http://timecenter.cs.aau.dk

� Positioning technologies

� 3GPP specifications, http://www.3gpp.org/specs/specs.htm

� Dana, P.H.: Global Positioning System Overview, http://www.colorado.edu/geography/gcraft/notes/gps/gps_f.html

� ETSI – European Telecommunication Standards Institute. http://www.etsi.org

� Open GIS Consortium, OpenGIS® Location Services (OpenLS): Core Services, http://www.openls.org

� Open Mobile Alliance (OMA), http://www.openmobilealliance.org

� OpenPrivacy Initiative, http://www.openprivacy.org

� Parlay Mobility Service Interface v.1.1, http://www.parlay.org

� Shostek group: The Future of WAP and Location Based Services, http://www.shosteck.com/studies/wap_lbs.htm

� Trimble: All About GPS, http://www.trimble.com/gps

Date post:	30-Jan-2020
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

Mobility Data Management - Aalborg...

Documents