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Mobility, Data Mining and Privacy: Geographic Knowledge Discovery Softcover reprint of hardcover 1st ed. 2008 [Pehme köide]

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  • Formaat: Paperback / softback, 410 pages, kõrgus x laius: 235x155 mm, kaal: 646 g, XIV, 410 p., 1 Paperback / softback
  • Ilmumisaeg: 19-Oct-2010
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642094430
  • ISBN-13: 9783642094439
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  • Formaat: Paperback / softback, 410 pages, kõrgus x laius: 235x155 mm, kaal: 646 g, XIV, 410 p., 1 Paperback / softback
  • Ilmumisaeg: 19-Oct-2010
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642094430
  • ISBN-13: 9783642094439
The technologies of mobile communications and ubiquitous computing are p- vading our society. Wireless networks are becoming the nerves of our territory, especially in the urban setting; through these nerves, the movement of people and vehicles may be sensed and possibly recorded, thus producing large volumes of mobility data. This is a scenario of great opportunities and risks. On one side, data mining can be put to work to analyse these data, with the purpose of producing useful knowledge in support of sustainable mobility and intelligent transportation systems. On the other side, individual privacy is at risk, as the mobility data may reveal, if misused, highly sensitive personal information. In a nutshell, a novel multi-disciplinary research area is emerging within this challenging con ict of opportunities and risks and at the crossroads of three s- jects: mobility, data mining and privacy. This book is aimed at shaping up this frontier of research, from a computer science perspective: we investigate the v- ious scienti c and technologicalachievementsthat are needed to face the challenge, anddiscussthecurrentstate oftheart,theopenproblemsandtheexpectedroad-map of research. Hence, this is a book for researchers: ?rst of all for computer science researchers, from any sub-area of the ?eld, and also for researchers from other disciplines (such as geography, statistics, social sciences, law, telecommunication and transportation engineering) who are willing to engage in a multi-disciplinary research area with potential for broad social and economic impact.
Mobility, Data Mining and Privacy: A Vision of Convergence 1(14)
F. Giannotti
D. Pedreschi
1 Mobility Data
2(1)
2 Data Mining
3(1)
3 Mobility Data Mining
4(4)
4 Privacy
8(1)
5 Purpose of This Book
9(6)
References
11(4)
Part I Setting the Stage
1 Basic Concepts of Movement Data
15(24)
N. Andrienko
G. Andrienko
N. Pelekis
S. Spaccapietra
1.1 Introduction
15(3)
1.2 Movement Data and Their Characteristics
18(7)
1.3 Analytical Questions
25(13)
1.4 Conclusion
38(1)
References
38(1)
2 Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process
39(34)
M. Wachowicz
A. Ligtenberg
C. Renso
S. Gurses
2.1 Introduction
39(2)
2.2 The Privacy-Aware Geographic Knowledge Discovery Process
41(2)
2.3 The Geographic Knowledge Discovery Process
43(4)
2.4 Reframing a GKDD Process Using a Multi-tier Ontological Perspective
47(4)
2.5 The Multi-tier Ontological Framework
51(9)
2.6 Future Application Domains for a Privacy-Aware GKDD Process
60(9)
2.7 Conclusions
69(4)
References
70(3)
3 Wireless Network Data Sources: Tracking and Synthesizing Trajectories
73(28)
C. Renso
S. Puntoni
E. Frentzos
A. Mazzoni
B. Moelans
N. Pelekis
F. Pini
3.1 Introduction
73(1)
3.2 Categorization of Positioning Technologies
74(9)
3.3 Mobile Location Systems
83(6)
3.4 From Positioning to Tracking: Collecting User Movements
89(2)
3.5 Synthetic Trajectory Generators
91(7)
3.6 Conclusions and Open Issues
98(3)
References
99(2)
4 Privacy Protection: Regulations and Technologies, Opportunities and Threats
101(22)
D. Pedreschi
F. Bonchi
F. Turini
V.S. Verykios
M. Atzori
B. Malin
B. Moelans
Y. Saygin
4.1 Introduction
101(5)
4.2 Privacy Regulations
106(8)
4.3 Privacy-Preserving Data Analysis
114(2)
4.4 The Role of the Observatory
116(1)
4.5 Conclusions
117(6)
References
118(5)
Part II Managing Moving Object and Trajectory Data
5 Trajectory Data Models
123(28)
J. Macedo
C. Vangenot
W. Othman
N. Pelekis
E. Frentzos
B. Kuijpers
I. Ntoutsi
S. Spaccapietra
Y. Theodoridis
5.1 Introduction
123(1)
5.2 Basic Concepts: From Raw Data to Trajectory
124(5)
5.3 Modelling Approaches for Trajectories
129(12)
5.4 Open Issues
141(10)
References
147(4)
6 Trajectory Database Systems
151(38)
E. Frentzos
N. Pelekis
I. Ntoutsi
Y. Theodoridis
6.1 Introduction
151(1)
6.2 Trajectory Database Engines
151(3)
6.3 Trajectory Indexing
154(5)
6.4 Trajectory Query Processing and Optimization
159(6)
6.5 Dealing with Location Uncertainty
165(5)
6.6 Handling Trajectory Compression
170(3)
6.7 Open Issues: Roadmap
173(10)
6.8 Concluding Remarks
183(6)
References
183(6)
7 Towards Trajectory Data Warehouses
189(24)
N. Pelekis
A. Raffaeta
M.-L. Damiani
C. Vangenot
G. Marketos
E. Frentzos
I. Ntoutsi
Y. Theodoridis
7.1 Introduction
189(2)
7.2 Preliminaries and Related Work
191(7)
7.3 Requirements for Trajectory Data Warehouses
198(8)
7.4 Modelling and Uncertainty Issues
206(3)
7.5 Conclusions
209(4)
References
210(3)
8 Privacy and Security in Spatiotemporal Data and Trajectories
213(30)
V.S. Verykios
M.L. Damiani
A. Gkoulalas-Divanis
8.1 Introduction
213(2)
8.2 State of the Art
215(16)
8.3 Open Issues, Future Work, and Road Map
231(7)
8.4 Conclusion
238(5)
References
238(5)
Part III Mining Spatiotemporal and Trajectory Data
9 Knowledge Discovery from Geographical Data
243(24)
S. Rinzivillo
F. Turini
V. Bogorny
C. Korner
B. Kuijpers
M. May
9.1 Introduction
243(1)
9.2 Geographic Data Representation and Modelling
244(2)
9.3 Geographic Information Systems
246(1)
9.4 Spatial Feature Extraction
247(6)
9.5 Spatial Data Mining
253(7)
9.6 Example: Frequency Prediction of Inner-City Traffic
260(1)
9.7 Roadmap to Knowledge Discovery from Spatiotemporal Data
261(2)
9.8 Summary
263(4)
References
263(4)
10 Spatiotemporal Data Mining
267(30)
M. Nanni
B. Kuijpers
C. Korner
M. May
D. Pedreschi
10.1 Introduction
267(1)
10.2 Challenges for Spatiotemporal Data Mining
268(2)
10.3 Clustering
270(6)
10.4 Spatiotemporal Local Patterns
276(8)
10.5 Prediction
284(5)
10.6 The Role of Uncertainty in Spatiotemporal Data Mining
289(1)
10.7 Conclusion
289(8)
References
292(5)
11 Privacy in Spatiotemporal Data Mining
297(38)
F. Bonchi
Y. Saygin
V.S. Verykios
M. Atzori
A. Gkoulalas-Divanis
S.V. Kaya
E. Savas
11.1 Introduction
297(3)
11.2 Data Perturbation and Obfuscation
300(4)
11.3 Knowledge Hiding
304(8)
11.4 Distributed Privacy-Preserving Data Mining
312(8)
11.5 Privacy-Aware Knowledge Sharing
320(5)
11.6 Roadmap Toward Privacy-Aware Mining of Spatiotemporal Data
325(3)
11.7 Conclusions
328(7)
References
329(6)
12 Querying and Reasoning for Spatiotemporal Data Mining
335(40)
G. Manco
M. Baglioni
F. Giannotti
B. Kuijpers
A. Raffaeta
C. Renso
12.1 Introduction
335(2)
12.2 Elements of a Data Mining Query Language
337(5)
12.3 DMQL Approaches in the Literature
342(16)
12.4 Querying Spatiotemporal Data
358(11)
12.5 Discussion
369(1)
12.6 Conclusions
370(5)
References
371(4)
13 Visual Analytics Methods for Movement Data
375
G. Andrienko
N. Andrienko
I. Kopanakis
A. Ligtenberg
S. Wrobel
13.1 Introduction
375(1)
13.2 State of the Art
376(7)
13.3 Patterns in Movement Data
383(5)
13.4 Helping Users to Detect Patterns: A Roadmap
388(13)
13.5 Visualization of Patterns
401(6)
13.6 Conclusion
407
References
408