Mobility, Data Mining and Privacy: Geographic Knowledge Discovery (Google eBook)

Front Cover
Fosca Giannotti, Dino Pedreschi
Springer, Jan 12, 2008 - Computers - 410 pages
0 Reviews
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.
  

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

Basic Concepts of Movement Data
15
12 Movement Data and Their Characteristics
18
13 Analytical Questions
25
14 Conclusion
38
Characterising the Next Generation of Mobile Applications Through a PrivacyAware Geographic Knowledge Discovery Process
39
22 The PrivacyAware Geographic Knowledge Discovery Process
41
23 The Geographic Knowledge Discovery Process
43
24 Reframing a GKDD Process Using a Multitier Ontological Perspective
47
Privacy and Security in Spatiotemporal Data and Trajectories
213
82 State of the Art
215
83 Open Issues Future Work and Road Map
231
84 Conclusion
238
Mining Spatiotemporal and Trajectory Data
242
Knowledge Discovery from Geographical Data
243
92 Geographic Data Representation and Modelling
244
93 Geographic Information Systems
246

25 The MultiTier Ontological Framework
51
26 Future Application Domains for a PrivacyAware GKDD Process
60
27 Conclusions
69
References
70
Wireless Network Data Sources Tracking and Synthesizing Trajectories
73
32 Categorization of Positioning Technologies
74
33 Mobile Location Systems
83
Collecting User Movements
89
35 Synthetic Trajectory Generators
91
36 Conclusions and Open Issues
98
References
99
Privacy Protection Regulations and Technologies Opportunities and Threats
101
42 Privacy Regulations
106
43 PrivacyPreserving Data Analysis
114
44 The Role of the Observatory
116
45 Conclusions
117
References
118
Managing Moving Object and Trajectory Data
120
Trajectory Data Models
123
From Raw Data to Trajectory
124
53 Modelling Approaches for Trajectories
129
54 Open Issues
141
References
147
Trajectory Database Systems
151
63 Trajectory Indexing
154
64 Trajectory Query Processing and Optimization
159
65 Dealing with Location Uncertainty
165
66 Handling Trajectory Compression
170
Roadmap
173
68 Concluding Remarks
183
Towards Trajectory Data Warehouses
189
72 Preliminaries and Related Work
191
73 Requirements for Trajectory Data Warehouses
198
74 Modelling and Uncertainty Issues
206
75 Conclusions
209
References
210
94 Spatial Feature Extraction
247
95 Spatial Data Mining
253
Frequency Prediction of InnerCity Traffic
260
97 Roadmap to Knowledge Discovery from Spatiotemporal Data
261
98 Summary
263
Spatiotemporal Data Mining
267
102 Challenges for Spatiotemporal Data Mining
268
103 Clustering
270
104 Spatiotemporal Local Patterns
276
105 Prediction
284
106 The Role of Uncertainty in Spatiotemporal Data Mining
289
References
292
Privacy in Spatiotemporal Data Mining
297
112 Data Perturbation and Obfuscation
300
113 Knowledge Hiding
304
114 Distributed PrivacyPreserving Data Mining
312
115 PrivacyAware Knowledge Sharing
320
116 Roadmap Toward PrivacyAware Mining of Spatiotemporal Data
325
117 Conclusions
328
References
329
Querying and Reasoning for Spatiotemporal Data Mining
335
122 Elements of a Data Mining Query Language
337
123 DMQL Approaches in the Literature
342
124 Querying Spatiotemporal Data
358
125 Discussion
369
126 Conclusions
370
References
371
Visual Analytics Methods for Movement Data
375
132 State of the Art
376
133 Patterns in Movement Data
383
A Roadmap
388
135 Visualization of Patterns
401
136 Conclusion
407
References
408
Copyright

Common terms and phrases

Popular passages

Page 3 - Science is built up with facts, as a house is with stones. But a collection of facts is no more a science than a heap of stones is a house.

References to this book