移動通信技術的持續發展催生了基於位置服務（
LBS）的廣泛應用。這類新型應用需要存儲並管理移
動對像不斷變化的位置信息。這本由孟小峰、丁治明
、許佳捷著的《移動對像管理(模型技術與應用第2版
英文版)(精)》針對移動對像數據管理問題，從位置
服務的角度分析頻繁的位置變化給傳統數據庫所帶來
的挑戰。本書繫統介紹了移動對像建模與位置跟蹤、
索引、查詢處理與優化、軌跡聚類、不確定性處理、
隱私保護等領域的最新研究成果，以及相關成果在智
能交通繫統中的應用。
本書的讀者對像為高等院校計算機專業的本科生
、研究生、教師，科研機構的研究人員以及相關領域
的開發人員等。

1 Introduction
1.1 Concept of MovingObjects Data Management
1.2 ApplicationsofMovingObjectsDatabase
1.3 Key Technologiesin Moving Objects Database
1.3.1 MovingObjects Modeling
1.3.2 Location Trackingof Moving Objects
1.3.3 MovingObjects Database Indexes
1.3.4 UncertaintyManagement
1.3.5 MovingObjectsDatabaseQuerying
1.3.6 Statistical Analysis and Data Mining of MovingObject Trajectories
1.3.7 LocationPrivacy
1.4 Applicationsof Mobile Data Management
1.5 Purposeof This Book
References
2 Moving Objects Modeling
2.1 Introduction
2.2 Representative Models
2.2.1 MovingObject Spatio-Temporal(MOST) Model
2.2.2 Abstract Data Type (ADT) with Network
2.2.3 Graph of Cellular Automata (GCA)
2.3 DTNMOM
2.4 ARS-DTNMOM
2.5 Summary
References
3 Moving Objects Tracking
3.1 Introduction
3.2 Representative Location Update Policies
3.2.1 Threshold-BasedLocation Updating
3.2.2 Motion Vector-Based Location Updating
3.2.3 Group-BasedLocation Updating
3.2.4 Network-ConstrainedLocation Updating
3.3 Network-ConstrainedMoving Objects Modeling and Tracking
3.3.1 Data Model for Network-ConstrainedMovingObjects
3.3.2 Location Update Strategies for Network-ConstrainedMoving Objects
3.4 A Traf.c-AdaptiveLocation Update Mechanism
3.4.1 The AutonomicANLUM (ANLUM-A) Method
3.4.2 The Centralized ANLUM (ANLUM-C) Method
3.5 A Hybrid Network-ConstrainedLocation Update Mechanism
3.6 Summary
References
4 Moving Objects Indexing
4.1 Introduction
4.2 Representative Indexing Methods
4.2.1 The R-Tree
4.2.2 The TPR-Tree
4.2.3 The Spatio-TemporalR-Tree
4.2.4 The Trajectory-BundleTree
4.2.5 The MON-Tree
4.3 Network-Constrained Moving Object Sketched-TrajectoryR-Tree
4.3.1 Data Model
4.3.2 IndexStructure
4.3.3 IndexUpdate
4.3.4 Query
4.4 Network-Constrained Moving Objects Dynamic Trajectory R-Tree
4.4.1 IndexStructure of NDTR-Tree
4.4.2 Active TrajectoryUnit Management
4.4.3 Constructing, Dynamic Maintaining, and Queryingof NDTR-Tree
4.5 Summary
References
5 Moving Objects Basic Querying
5.1 Introduction
5.2 Classi.cations of Moving Object Queries
5.2.1 Based on Spatial Predicates
5.2.2 Based on TemporalPredicates
5.2.3 Based on Moving Spaces
5.3 Point Queries
5.4 NN Queries
5.4.1 Incremental Euclidean Restriction
5.4.2 Incremental Network Expansion
5.5 Range Queries
5.5.1 Range Euclidean Restriction
5.5.2 Range Network Expansion
5.6 Summary
References
6 Moving Objects Advanced Querying
6.1 Introduction
6.2 Similar Trajectory Queries for Moving Objects
6.2.1 Problem Definition
6.2.2 Trajectory Similarity
6.2.3 Query Processing
6.3 Convoy Queries on Moving Objects
6.3.1 Spatial Relations AmongConvoy Objects
6.3.2 Coherent Moving Cluster(CMC)
6.3.3 Convoy Over Simplified Trajectory (CoST)
6.3.4 Spatio-TemporalExtension (CoST*)
6.4 Density Queries for MovingObjects in Spatial Networks
6.4.1 Problem Definition
6.4.2 Cluster-Based Query Preprocessing
6.4.3 Density Query Processing
6.5 Continuous Density Queries for Moving Objects
6.5.1 Problem De.nition
6.5.2 Building the Quad-Tree
6.5.3 Safe Interval Computation
6.5.4 Query Processing
6.6 Summary
References
7 Trajectory Prediction of Moving Objects
7.1 Introduction
7.2 UnderlyingLinear Prediction (LP) Methods
7.2.1 General Linear Prediction
7.2.2 Road Segment-Based Linear Prediction
7.2.3 Route-Based Linear Prediction
7.3 Simulation-Based Prediction (SP) Methods
7.3.1 Fast-Slow Bounds Prediction
7.3.2 Time-Segmented Prediction
7.4 Uncertain Path Prediction Methods
7.4.1 Preliminary
7.4.2 Uncertain Trajectory Pattern Mining Algorithm
7.4.3 Frequent Path Tree
7.4.4 Trajectory Prediction
7.5 Other Nonlinear Prediction Methods
7.6 Summary
References
8 Uncertainty Management in Moving Objects Database
8.1 Introduction
8.2 Representative Models
8.2.1 2D-Ellipse Model
8.2.2 3D-Cylinder Model
8.2.3 Modelthe Uncertainty in Database
8.3 Uncertain Trajectory Management
8.3.1 Uncertain Trajectory Modeling
8.3.2 Database Operations for Uncertainty Management
8.4 Summary
References
9 Statistical Analysis on Moving Object Trajectories
9.1 Introduction
9.2 Representative Methods
9.2.1 Based on FCDs
9.2.2 Based on MODs
9.3 Real-Time Traffic Analysis on Dynamic Transportation Net
9.3.1 ModelingDynamic TransportationNetworks
9.3.2 Real-Time Statistical Analysis of Traffic Parameters
9.4 Summary
References
10 Clustering Analysis of Moving Objects
10.1 Introduction
10.2 Underlying Clustering Analysis Methods
10.3 Clustering Static Objects in Spatial Networks
10.3.1 Problem Definition
10.3.2 Edge-Based Clustering Algorithm
10.3.3 Node-Based Clustering Algorithm
10.4 Clustering MovingObjects in Spatial Networks
10.4.1 CMON Framework
10.4.2 Constructionand Maintenance of CBs
10.4.3 CMON Construction with Different Criteria
10.5 Clustering Trajectories Based on Partition-and-Group
10.5.1 Partition-and-Group Framework
10.5.2 Region-Based Cluster
10.5.3 Trajectory-Based Cluster
10.6 Clustering TrajectoriesBased on Features Other Than Density
10.6.1 Preliminary
10.6.2 Big Region Reconstruction
10.6.3 Parameters Determinationin Region Refinement
10.7 Summary
References
11 Dynamic Transportation Navigation
11.1 Introduction
11.2 TypicalDynamicTransportationNavigationStrategies
11.2.1 D* Algorithm
11.2.2 Hierarchy Aggregation Tree Based Navigation
11.3 Incremental Route Search Strategy
11.3.1 Problem Definitions
11.3.2 Pre-computation
11.3.3 Top-KIntermediate Destinations
11.3.4 Route Search and Update
11.4 Summary
References
12 Location Privacy
12.1 Introduction
12.2 Privacy Threats in LBS
12.3 System Architecture
12.3.1 Non-cooperative Architecture
12.3.2 Centralized Architecture
12.3.3 Peer-to-Peer Architecture
12.4 Location Anonymization Techniques
12.4.1 Location K-Anonymity Model
12.4.2 p-Sensitivity Model
12.4.3 Anonymization Algorithms
12.5 Evaluation Metrics
12.6 Summary
References
Index