Mobility, Data Mining and Privacy: A Vision of Convergence |
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1 Basic Concepts of Movement Data |
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1.2 Movement Data and Their Characteristics |
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2 Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process |
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2.2 The Privacy-Aware Geographic Knowledge Discovery Process |
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2.3 The Geographic Knowledge Discovery Process |
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43 | (4) |
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2.4 Reframing a GKDD Process Using a Multi-tier Ontological Perspective |
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47 | (4) |
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2.5 The Multi-tier Ontological Framework |
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51 | (9) |
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2.6 Future Application Domains for a Privacy-Aware GKDD Process |
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69 | (4) |
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70 | (3) |
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3 Wireless Network Data Sources: Tracking and Synthesizing Trajectories |
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3.2 Categorization of Positioning Technologies |
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3.3 Mobile Location Systems |
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3.4 From Positioning to Tracking: Collecting User Movements |
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3.5 Synthetic Trajectory Generators |
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3.6 Conclusions and Open Issues |
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4 Privacy Protection: Regulations and Technologies, Opportunities and Threats |
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101 | (5) |
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4.3 Privacy-Preserving Data Analysis |
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4.4 The Role of the Observatory |
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118 | (5) |
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Part II Managing Moving Object and Trajectory Data |
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123 | (1) |
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5.2 Basic Concepts: From Raw Data to Trajectory |
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124 | (5) |
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5.3 Modelling Approaches for Trajectories |
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147 | (4) |
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6 Trajectory Database Systems |
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151 | (1) |
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6.2 Trajectory Database Engines |
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151 | (3) |
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154 | (5) |
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6.4 Trajectory Query Processing and Optimization |
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159 | (6) |
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6.5 Dealing with Location Uncertainty |
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165 | (5) |
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6.6 Handling Trajectory Compression |
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170 | (3) |
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7 Towards Trajectory Data Warehouses |
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189 | (2) |
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7.2 Preliminaries and Related Work |
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7.3 Requirements for Trajectory Data Warehouses |
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7.4 Modelling and Uncertainty Issues |
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206 | (3) |
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209 | (4) |
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210 | (3) |
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8 Privacy and Security in Spatiotemporal Data and Trajectories |
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213 | (30) |
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213 | (2) |
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215 | (16) |
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8.3 Open Issues, Future Work, and Road Map |
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231 | (7) |
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238 | (5) |
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238 | (5) |
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Part III Mining Spatiotemporal and Trajectory Data |
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9 Knowledge Discovery from Geographical Data |
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243 | (24) |
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243 | (1) |
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9.2 Geographic Data Representation and Modelling |
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244 | (2) |
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9.3 Geographic Information Systems |
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246 | (1) |
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9.4 Spatial Feature Extraction |
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253 | (7) |
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9.6 Example: Frequency Prediction of Inner-City Traffic |
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9.7 Roadmap to Knowledge Discovery from Spatiotemporal Data |
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10 Spatiotemporal Data Mining |
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267 | (30) |
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10.2 Challenges for Spatiotemporal Data Mining |
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268 | (2) |
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10.4 Spatiotemporal Local Patterns |
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276 | (8) |
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284 | (5) |
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10.6 The Role of Uncertainty in Spatiotemporal Data Mining |
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289 | (1) |
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289 | (8) |
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292 | (5) |
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11 Privacy in Spatiotemporal Data Mining |
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297 | (38) |
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297 | (3) |
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11.2 Data Perturbation and Obfuscation |
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300 | (4) |
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304 | (8) |
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11.4 Distributed Privacy-Preserving Data Mining |
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312 | (8) |
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11.5 Privacy-Aware Knowledge Sharing |
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320 | (5) |
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11.6 Roadmap Toward Privacy-Aware Mining of Spatiotemporal Data |
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325 | (3) |
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328 | (7) |
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329 | (6) |
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12 Querying and Reasoning for Spatiotemporal Data Mining |
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335 | (40) |
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335 | (2) |
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12.2 Elements of a Data Mining Query Language |
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337 | (5) |
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12.3 DMQL Approaches in the Literature |
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12.4 Querying Spatiotemporal Data |
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358 | (11) |
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369 | (1) |
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370 | (5) |
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13 Visual Analytics Methods for Movement Data |
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375 | (1) |
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376 | (7) |
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13.3 Patterns in Movement Data |
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383 | (5) |
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13.4 Helping Users to Detect Patterns: A Roadmap |
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388 | (13) |
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13.5 Visualization of Patterns |
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