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E-grāmata: Moving Objects Management: Models, Techniques and Applications

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  • Formāts: PDF+DRM
  • Izdošanas datums: 04-Apr-2014
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642382765
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Moving Objects Management: Models, Techniques and ApplicationsPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 04-Apr-2014
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783642382765
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Applications, 2nd Edition focuses on moving object management, from the location management perspective to determining how constantly changing locations affect the traditional database and data mining technology. The book specifically describes the topics of moving objects modeling and location tracking, indexing and querying, clustering, location uncertainty, traffic-aware navigation and privacy issues, as well as the application to intelligent transportation systems. Through the book, the readers will be made familiar with the cutting-edge technologies in moving object management that can be effectively applied in LBS and transportation contexts. The second edition of this book significantly expands the coverage of the latest research on location privacy, traffic-aware navigation and uncertainty. The book has also been reorganized, with nearly all chapters rewritten, and several new chapters have been added to address the latest topics on moving objects management.

Xiaofeng Meng is a professor at the School of Information, Renmin University of China; Zhiming Ding is a professor at the Institute of Software, Chinese Academy of Sciences (ISCAS); Jiajie Xu is an assistant professor at the ISCAS.



This book describes the topics of moving objects modeling and location tracking, indexing and querying, clustering, location uncertainty, traffic aware navigation and privacy issues as well as the application to intelligent transportation systems.
1 Introduction
1(14)
1.1 Concept of Moving Objects Data Management
1(1)
1.2 Applications of Moving Objects Database
2(1)
1.3 Key Technologies in Moving Objects Database
3(6)
1.3.1 Moving Objects Modeling
3(1)
1.3.2 Location Tracking of Moving Objects
4(2)
1.3.3 Moving Objects Database Indexes
6(1)
1.3.4 Uncertainty Management
7(1)
1.3.5 Moving Objects Database Querying
7(1)
1.3.6 Statistical Analysis and Data Mining of Moving Object Trajectories
8(1)
1.3.7 Location Privacy
9(1)
1.4 Applications of Mobile Data Management
9(1)
1.5 Purpose of This Book
10(5)
References
10(5)
2 Moving Objects Modeling
15(18)
2.1 Introduction
15(2)
2.2 Representative Models
17(4)
2.2.1 Moving Object Spatio-Temporal (MOST) Model
17(1)
2.2.2 Abstract Data Type (ADT) with Network
18(2)
2.2.3 Graph of Cellular Automata (GCA)
20(1)
2.3 DTNMOM
21(5)
2.4 ARS-DTNMOM
26(4)
2.5 Summary
30(3)
References
30(3)
3 Moving Objects Tracking
33(18)
3.1 Introduction
33(1)
3.2 Representative Location Update olicies
34(2)
3.2.1 Threshold-Based Location Updating
34(1)
3.2.2 Motion Vector-Based Location Updating
35(1)
3.2.3 Group-Based Location Updating
35(1)
3.2.4 Network-Constrained Location Updating
36(1)
3.3 Network-Constrained Moving Objects Modeling and Tracking
36(4)
3.3.1 Data Model for Network-Constrained Moving Objects
36(2)
3.3.2 Location Update Strategies for Network-Constrained Moving Objects
38(2)
3.4 A Traffic-Adaptive Location Update Mechanism
40(7)
3.4.1 The Autonomic ANLUM (ANLUM-A) Method
42(2)
3.4.2 The Centralized ANLUM (ANLUM-C) Method
44(3)
3.5 A Hybrid Network-Constrained Location Update Mechanism
47(1)
3.6 Summary
48(3)
References
49(2)
4 Moving Objects Indexing
51(22)
4.1 Introduction
51(2)
4.2 Representative Indexing Methods
53(6)
4.2.1 The R-Tree
53(1)
4.2.2 The TPR-Tree
54(2)
4.2.3 The Spatio-Temporal R-Tree
56(1)
4.2.4 The Trajectory-Bundle Tree
57(1)
4.2.5 The MON-Tree
58(1)
4.3 Network-Constrained Moving Object Sketched-Trajectory R-Tree
59(8)
4.3.1 Data Model
60(1)
4.3.2 Index Structure
61(3)
4.3.3 Index Update
64(1)
4.3.4 Query
65(2)
4.4 Network-Constrained Moving Objects Dynamic Trajectory R-Tree
67(4)
4.4.1 Index Structure of NDTR-Tree
67(1)
4.4.2 Active Trajectory Unit Management
68(2)
4.4.3 Constructing, Dynamic Maintaining, and Querying of NDTR-Tree
70(1)
4.5 Summary
71(2)
References
72(1)
5 Moving Objects Basic Querying
73(14)
5.1 Introduction
73(1)
5.2 Classifications of Moving Object Queries
74(3)
5.2.1 Based on Spatial Predicates
74(2)
5.2.2 Based on Temporal Predicates
76(1)
5.2.3 Based on Moving Spaces
76(1)
5.3 Point Queries
77(1)
5.4 NN Queries
78(3)
5.4.1 Incremental Euclidean Restriction
78(1)
5.4.2 Incremental Network Expansion
79(2)
5.5 Range Queries
81(2)
5.5.1 Range Euclidean Restriction
81(1)
5.5.2 Range Network Expansion
82(1)
5.6 Summary
83(4)
References
84(3)
6 Moving Objects Advanced Querying
87(30)
6.1 Introduction
87(2)
6.2 Similar Trajectory Queries for Moving Objects
89(6)
6.2.1 Problem Definition
90(2)
6.2.2 Trajectory Similarity
92(2)
6.2.3 Query Processing
94(1)
6.3 Convoy Queries on Moving Objects
95(4)
6.3.1 Spatial Relations Among Convoy Objects
96(1)
6.3.2 Coherent Moving Cluster (CMC)
96(1)
6.3.3 Convoy Over Simplified Trajectory (CoST)
96(2)
6.3.4 Spatio-Temporal Extension (CoST*)
98(1)
6.4 Density Queries for Moving Objects in Spatial Networks
99(6)
6.4.1 Problem Definition
99(1)
6.4.2 Cluster-Based Query Preprocessing
100(2)
6.4.3 Density Query Processing
102(3)
6.5 Continuous Density Queries for Moving Objects
105(7)
6.5.1 Problem Definition
106(1)
6.5.2 Building the Quad-Tree
107(1)
6.5.3 Safe Interval Computation
108(4)
6.5.4 Query Processing
112(1)
6.6 Summary
112(5)
References
113(4)
7 Trajectory Prediction of Moving Objects
117(16)
7.1 Introduction
117(1)
7.2 Underlying Linear Prediction (LP) Methods
118(2)
7.2.1 General Linear Prediction
118(1)
7.2.2 Road Segment-Based Linear Prediction
118(1)
7.2.3 Route-Based Linear Prediction
119(1)
7.3 Simulation-Based Prediction (SP) Methods
120(3)
7.3.1 Fast-Slow Bounds Prediction
120(3)
7.3.2 Time-Segmented Prediction
123(1)
7.4 Uncertain Path Prediction Methods
123(7)
7.4.1 Preliminary
124(2)
7.4.2 Uncertain Trajectory Pattern Mining Algorithm
126(1)
7.4.3 Frequent Path Tree
127(3)
7.4.4 Trajectory Prediction
130(1)
7.5 Other Nonlinear Prediction Methods
130(1)
7.6 Summary
131(2)
References
131(2)
8 Uncertainty Management in Moving Objects Database
133(16)
8.1 Introduction
133(2)
8.2 Representative Models
135(5)
8.2.1 2D-EUipse Model
135(1)
8.2.2 3D-Cylinder Model
136(1)
8.2.3 Model the Uncertainty in Database
137(3)
8.3 Uncertain Trajectory Management
140(7)
8.3.1 Uncertain Trajectory Modeling
140(4)
8.3.2 Database Operations for Uncertainty Management
144(3)
8.4 Summary
147(2)
References
147(2)
9 Statistical Analysis on Moving Object Trajectories
149(14)
9.1 Introduction
149(2)
9.2 Representative Methods
151(1)
9.2.1 Based on FCDs
151(1)
9.2.2 Based on MODs
151(1)
9.3 Real-Time Traffic Analysis on Dynamic Transportation Networks
152(8)
9.3.1 Modeling Dynamic Transportation Networks
152(4)
9.3.2 Real-Time Statistical Analysis of Traffic Parameters
156(4)
9.4 Summary
160(3)
References
161(2)
10 Clustering Analysis of Moving Objects
163(34)
10.1 Introduction
163(1)
10.2 Underlying Clustering Analysis Methods
164(2)
10.3 Clustering Static Objects in Spatial Networks
166(9)
10.3.1 Problem Definition
167(1)
10.3.2 Edge-Based Clustering Algorithm
168(4)
10.3.3 Node-Based Clustering Algorithm
172(3)
10.4 Clustering Moving Objects in Spatial Networks
175(8)
10.4.1 CMON Framework
176(1)
10.4.2 Construction and Maintenance of CBs
177(2)
10.4.3 CMON Construction with Different Criteria
179(4)
10.5 Clustering Trajectories Based on Partition-and-Group
183(5)
10.5.1 Partition-and-Group Framework
183(3)
10.5.2 Region-Based Cluster
186(1)
10.5.3 Trajectory-Based Cluster
187(1)
10.6 Clustering Trajectories Based on Features Other Than Density
188(5)
10.6.1 Preliminary
188(2)
10.6.2 Big Region Reconstruction
190(3)
10.6.3 Parameters Determination in Region Refinement
193(1)
10.7 Summary
193(4)
References
194(3)
11 Dynamic Transportation Navigation
197(14)
11.1 Introduction
197(2)
11.2 Typical Dynamic Transportation Navigation Strategies
199(2)
11.2.1 D* Algorithm
199(1)
11.2.2 Hierarchy Aggregation Tree Based Navigation
200(1)
11.3 Incremental Route Search Strategy
201(6)
11.3.1 Problem Definitions
201(2)
11.3.2 Pre-computation
203(1)
11.3.3 Top-K Intermediate Destinations
204(2)
11.3.4 Route Search and Update
206(1)
11.4 Summary
207(4)
References
207(4)
12 Location Privacy
211(16)
12.1 Introduction
211(1)
12.2 Privacy Threats in LBS
212(3)
12.3 System Architecture
215(2)
12.3.1 Non-cooperative Architecture
215(1)
12.3.2 Centralized Architecture
216(1)
12.3.3 Peer-to-Peer Architecture
217(1)
12.4 Location Anonymization Techniques
217(6)
12.4.1 Location K-Anonymity Model
218(1)
12.4.2 p-Sensitivity Model
219(3)
12.4.3 Anonymization Algorithms
222(1)
12.5 Evaluation Metrics
223(1)
12.6 Summary
224(3)
References
224(3)
Index 227
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