Computing with Spatial Trajectories
Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extr...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
New York, NY
Springer Science + Business Media
2011
Springer Springer New York |
| Edition | 1. Aufl. |
| Subjects | |
| Online Access | Get full text |
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Table of Contents:
- 5.6.1.2 Local Distance Measures -- MBR-Based Distance -- Trajectory-Hausdorff Distance -- Trajectory-Segment Distance -- 5.6.2 Techniques for Ef.cient Pattern Discovery -- 5.6.2.1 Raw Data Transformation -- The REMO Matrix -- Trajectory Simpliˇcation -- 5.6.2.2 Indexing -- 5.6.2.3 The Apriori Approach -- 5.7 Summary -- References -- Chapter 6 Activity Recognition from Trajectory Data -- 6.1 Introduction -- 6.2 Location Estimation for Obtaining the Trajectory Data -- 6.2.1 Propagation Models for Outdoor Location Estimation -- 6.2.2 Indoor Location Estimation using Learning-based Models -- 6.2.2.1 Nearest Neighbor Based Methods -- 6.2.2.2 Bayesian Methods -- 6.2.2.3 Summary -- 6.3 Trajectory-based Activity Recognition -- 6.3.1 Single-user Activity Recognition -- 6.3.1.1 Supervised Learning -- Decision Tree with Sequence Smoothing and Hidden Markov Model -- Dynamic Bayesian Networks -- Conditional Random Fields -- Summary -- 6.3.1.2 Unsupervised Learning -- Clustering Methods -- Principal Component Analysis -- Latent Dirichlet Allocation -- Summary -- 6.3.1.3 Frequent Pattern Mining -- 6.3.2 Multiple-user Activity Recognition -- Coupled Hidden Markov Model for Concurrent Activity Recognition -- Ensemble Learning -- Transfer Learning -- Factorial Conditional Random Fields -- Latent Aspect Model -- Summary and Outlook -- 6.4 Summary -- References -- Chapter 7 Trajectory Analysis for Driving -- 7.1 Introduction -- 7.2 Making Road Maps from Trajectories -- 7.2.1 Routable Road Map -- 7.2.1.1 Clarifying GPS Traces -- 7.2.1.2 Merging Traces -- 7.2.1.3 Routable Road Network -- 7.2.2 Intersection Detection -- 7.2.2.1 Detecting Intersections -- 7.2.2.2 Reˇning Intersections -- 7.2.3 Finding Traf.c Lanes -- 7.3 Map Matching -- 7.3.1 Hidden Markov Model for Map Matching -- 7.4 Destination Prediction -- 7.4.1 Destination Likelihood from Ef.cient Driving
- 7.4.2 Destination Priors -- 7.4.2.1 Driving Time -- 7.4.2.2 Ground Cover -- 7.4.2.3 Other Priors -- 7.4.3 Route Prediction -- 7.5 Learning Routes -- 7.5.1 T-Drive: Learn from Taxis -- 7.5.2 Learn from Yourself -- 7.6 Summary -- References -- Chapter 8 Location-Based Social Networks: Users -- 8.1 Introduction -- 8.1.1 Concepts and De.nitions of LBSNs -- 8.1.2 Location-Based Social Networking Services -- 8.1.3 Research Philosophy of LBSN -- 8.2 Modeling Human Location History -- 8.2.1 Overview -- 8.2.2 Geospatial Model Representing User Location History -- 8.2.3 Semantic Model Representing User Location History -- 8.3 Mining User Similarity Based on Location History -- 8.3.1 Motivation and Overview -- 8.3.2 Detecting Similar Sequences -- 8.3.3 Calculating Similarity Scores -- 8.4 Friend Recommendation and Community Discovery -- 8.4.1 Methodology -- 8.4.2 Public Datasets for the Evaluation -- 8.4.3 Methods for Obtaining Ground Truth -- 8.4.4 Metrics for the Evaluation -- 8.5 Summary -- References -- Chapter 9 Location-Based Social Networks: Locations -- 9.1 Introduction -- 9.2 Generic Travel Recommendations -- 9.2.1 Mining Interesting Locations and Travel Sequences -- 9.2.1.1 Background -- 9.2.1.2 Methodology for Mining Interesting Locations -- 9.2.1.3 Methodology for detecting travel sequences -- 9.2.2 Itinerary Recommendation -- 9.2.2.1 Background -- 9.2.2.2 Methodology for Itinerary Recommendation -- 9.2.3 Location-Activity Recommendation -- 9.2.3.1 Data Modeling -- 9.2.3.2 Collaborative Inference -- 9.3 Personalized Travel Recommendations -- 9.3.1 Collaborative Filtering -- 9.3.2 Location Recommenders Using User-Based CF -- 9.3.3 Location Recommenders Using Item-Based CF -- 9.3.3.1 Mining the Correlation between Locations -- 9.3.3.2 Rating Inference -- 9.3.4 Open Challenges -- 9.3.4.1 Cold Start -- 9.3.4.2 Data Sparseness -- 9.3.4.3 Scalability
- Chapter 3 Uncertainty in Spatial Trajectories -- 3.1 Introduction -- 3.2 Uncertainty Throughout the History -- 3.2.1 Philosophy and Logic -- 3.2.2 Uncertainty in AI and Databases -- 3.2.3 Time-Geography and Inexact Geometries -- 3.3 Uncertainty in Spatial and Temporal Databases -- 3.3.1 Spatial Databases -- 3.3.2 Temporal Databases -- 3.4 Modelling Uncertain Trajectories -- 3.4.1 Cones, Beads and Necklaces -- 3.4.2 Sheared Cylinders -- 3.4.3 Uncertainty on Road Networks -- 3.5 Processing Spatio-Temporal Queries for Uncertain Trajectories -- 3.5.1 Range Queries for Uncertain Trajectories -- 3.5.1.1 Instantaneous Range Query for Cones -- 3.5.1.2 Continuous Range Queries for Sheared Cylinders -- 3.5.1.3 Continuous Range Queries for Beads/Necklaces -- 3.5.1.4 Uncertain Range Queries on Road Networks -- 3.5.2 Nearest-Neighbor Queries for Uncertain Trajectories -- 3.5.2.1 NN Query for Cone Uncertainty Model -- 3.5.2.2 NN Query for Sheared Cylinders - Continuity and Time-Parameterization -- 3.5.2.3 NN Query for Beads Uncertainty Model -- 3.5.2.4 NN Query for Uncertain Trajectories on Road Networks -- 3.5.3 Potpourri: Some Miscellaneous Queries/Predicates for Uncertain Trajectories -- 3.6 Summary -- References -- Chapter 4 Privacy of Spatial Trajectories -- 4.1 Introduction -- 4.2 The Derivation of Spatial Trajectory Privacy -- 4.2.1 Data Privacy -- 4.2.2 Location Privacy -- 4.2.3 Trajectory Privacy -- 4.3 Protecting Trajectory Privacy in Location-based Services -- 4.3.1 Spatial Cloaking -- 4.3.1.1 Group-based Approach for Real-time Trajectory Data -- 4.3.1.2 Distortion-based Approach for Real-time Trajectory Data -- 4.3.1.3 Predication-based Approach for Historical Trajectory Data -- 4.3.2 Mix-Zones -- 4.3.3 Vehicular Mix-Zones -- 4.3.4 Path Confusion -- 4.3.5 Path Confusion with Mobility Prediction and Data Caching
- 4.3.6 Euler Histogram-based on Short IDs -- 4.3.7 Dummy Trajectories -- 4.4 Protecting Privacy in Trajectory Publication -- 4.4.1 Clustering-based Anonymization Approach -- 4.4.2 Generalization-based Anonymization Approach -- 4.4.3 Suppression-based Anonymization Approach -- 4.4.4 Grid-based Anonymization Approach -- 4.5 Summary -- References -- Chapter 5 Trajectory Pattern Mining -- 5.1 Introduction -- 5.2 Overview of Trajectory Patterns -- 5.2.1 Pattern Discovery Process -- 5.2.2 Classi.cation of Trajectory Pattern Concepts and Techniques -- 5.2.2.1 Mining Tasks on Trajectories -- Clustering of Trajectories -- Trajectory Join -- 5.2.2.2 Spatial and Spatiotemporal Patterns -- 5.2.2.3 Granularity of Trajectory Patterns -- Global Vs. Partial Patterns -- Individual Vs. Group Patterns -- 5.2.2.4 Constrained Trajectory Patterns -- Spatial Constraints: Movement on Spatial Networks -- Temporal Constraints: Periodicity -- 5.3 Relative Motion Patterns -- 5.3.1 Basic Motion Patterns -- Constance -- Concurrence -- Trendsetter -- 5.3.2 Spatial Motion Patterns -- Track -- Flock -- Leadership -- 5.3.3 Aggregate/Segregate Motion Patterns -- Convergence -- Encounter -- Divergence -- Breakup -- 5.3.4 Discussion -- 5.4 Disc-Based Trajectory Patterns -- 5.4.1 Prospective Patterns -- Encounter (m -- r) -- Convergence (m -- r) -- 5.4.2 Flock-Driven Patterns -- Flock (m -- r -- k) -- Meet (m -- r -- k) -- Leadership (m -- r -- k) -- 5.4.3 Discussion -- 5.5 Density-Based Trajectory Patterns -- 5.5.1 Density Notions -- 5.5.2 Moving Objects Clustering -- 5.5.2.1 TRACLUS -- 5.5.2.2 Moving Cluster -- 5.5.2.3 Convoy -- convoy Variants -- 5.5.2.4 Swarm -- Variants of Swarm -- 5.5.3 Discussion -- 5.6 Methods for Mining Trajectory Patterns -- 5.6.1 Trajectory Distance Measures -- 5.6.1.1 Global Distance Measures -- Euclidean Distance -- Alignment-based Distance
- Intro -- Computing with Spatial Trajectories -- Foreword -- Preface -- Acknowledgements -- Contents -- List of Contributors -- Acronyms -- Part I Foundations -- Chapter 1 Trajectory Preprocessing -- 1.1 Introduction -- 1.2 Trajectory Data Generation -- 1.3 Performance Metrics and Error Measures -- 1.4 Batched Compression Techniques -- 1.5 On-Line Data Reduction Techniques -- 1.6 Trajectory Data Reduction Based on Speed and Direction -- 1.7 Trajectory Filtering -- 1.7.1 Sample Data -- 1.7.2 Trajectory Model -- 1.8 Mean and Median Filters -- 1.9 Kalman Filter -- 1.9.1 Measurement Model -- 1.9.2 Dynamic Model -- 1.9.3 Entire Kalman Filter Model -- 1.9.4 Kalman Filter -- 1.9.5 Kalman Filter Discussion -- 1.10 Particle Filter -- 1.10.1 Particle Filter Formulation -- 1.10.2 Particle Filter -- 1.10.3 Particle Filter Discussion -- 1.11 Summary -- References -- Chapter 2 Trajectory Indexing and Retrieval -- 2.1 Introduction -- 2.2 Trajectory Query Types -- 2.2.1 P-Query -- 2.2.1.1 Ask for POI -- 2.2.1.2 Ask for Trajectory -- 2.2.2 R-Query -- 2.2.2.1 Ask for Trajectory -- 2.2.2.2 Ask for Regions -- 2.2.3 T-Query -- 2.2.3.1 Ask for Similar Trajectories -- 2.2.3.2 Ask for Close Trajectories -- 2.2.4 Applications -- 2.3 Trajectory Similarity Measures -- 2.3.1 Point to Trajectory -- 2.3.2 Trajectory to Trajectory -- 2.4 Trajectory Indexes -- 2.4.1 Augmented R-tree -- 2.4.2 Multiversion R-trees -- 2.4.3 Grid Based Index -- 2.5 Query Processing -- 2.5.1 Query Processing in Spatial Databases -- 2.5.2 P-query -- 2.5.2.1 Querying trajectories by a given point -- 2.5.2.2 Querying points by a given trajectory -- 2.5.3 T-Query -- 2.5.3.1 Finding trajectories from multiple points [10] -- 2.5.4 R-Query -- 2.5.4.1 R-Query with multi-version R-trees -- 2.5.4.2 R-Query with 3D R-trees -- 2.6 Summary -- References -- Part II Advanced Topics
- 9.4 Summary