移动对象管理

移动通信技术的持续发展催生了基于位置服务（LBS）的广泛应用。这类新型应用需要存储并管理移动对象不断变化的位置信息。本书针对移动对象数据管理问题，从位置服务的角度分析频繁的位置变化给传统数据库所带来的挑战。本书系统介绍了移动对象建模与位置跟踪、索引、查询处理与优化、轨迹聚类、不确定性处理、隐私保护等领域的最新研究成果，以及相关成果在智能交通系统中的应用。
本书的读者对象为高等院校计算机专业的本科生、研究生、教师，科研机构的研究人员以及相关领域的开发人员等。

1Introduction
1.1Concept of Moving Objects Data Management
1.2Applications of Moving Objects Databas
1.3 Key Technologies in Moving Objects Database
1.3.1 Moving Objects Modeling
1.3.2Location Tracking of Moving Objects
1.3.3Moving Objects Database Indexes
1.3.4 Uncertainty Management
1.3.5Moving Objects Database Querying
1.3.6Statistical Analysis and Data Mining of Moving Object Trajectories
1.3.7 Location Privacy
1.4 Applications of Mobile Data Management
1.5Purpose of This Book
References
2 Moving Objects Modeling
2.1 Introduction
2.2 Representative Models
2.2.1Moving Object Spatio-Temporal（MOST） Model
2.2.2 Abstract Data Type （ADT） with Network
2.2.3 Graph of Cellular Automata （GCA）
2.3DTNMOM
2.4 ARS-DTNMOM
2.5Summary
References
3 Moving Objects Tracking
3.1 Introduction
3.2 Representative Location Update Policies
3.2.1Threshold-Based Location Updating
3.2.2 Motion Vector-Based Location Updating
3.2.3 Group-Based Location Updating
3.2.4 Network-Constrained Location Updating
3.3 Network-Constrained Moving Objects Modeling and Tracking
3.3.1 Data Modelfor Network-Constrained Moving Objects
3.3.2 Location Update Strategies forNetwork-Constrained Moving Objects
3.4 A Traffic-Adaptive Location Update Mechanism
3.4.1The Autonomic ANLUM （ANLUM-A） Method
3.4.2The Centralized ANLUM （ANLUM-C） Method
3.5 A Hybrid Network-Constrained Location Update Mechanism
3.6Summary
References
4Moving Objects Indexing
4.1Introduction
4.2Representative Indexing Methods
4.2.1The R-Tree
4.2.2 The TPR-Tree
4.2.3The Spatio-TemporaI R-Tree
4.2.4 The Trajectory-Bundle Tree
4.2.5 TheMON-Tree
4.3 Network-Constrained Moving Object
Sketched-Trajectory R-Tree
4.3.1 Data Model
4.3.2 Index Structure
4.3.3Index Update
4.3.4 Que
4.4Network-Constrained Moving Objects Dynamic
Trajectory R-Tree
4.4.1 Index Structure of NDTR-Tree
4.4.2Active Trajectory Unit Management
4.4.3 Constructing， Dynamic Maintaining Querying of NDTR-Tree
4.5Summary
References
5Moving Objects Basic Querying
5.1Introduction
5.2 Classifications of Moving Object Queries
5.2.1 Based on Spatial Predicates
5.2.2 Based on Temporal Predicates
5.2.3Based on Moving Spaces
5.3Point Queries
5.4NN Queries
5.4.1Incremental Euclidean Restrictio
5.4.2IncrementalNetworkExpansion
5.5Range Queries
5.5.1Range Euclidean Restriction
5.5.2 Range Network Expansion
5.6Summary
References
6Moving Objects Advanced Querying
6.1Introduction
6.2 Similar Trajectory Queries for Moving Objects
6.2.1 Problem Definition
6.2.2Trajectory Similarity
6.2.3Query Processing
6.3 Convoy Queries on Moving Objects
6.3.1Spatial Relations Among Convoy Objects
6.3.2 Coherent Moving Cluster （CMC）
6.3.3 Convoy Over Simplified Trajectory （CoST）
6.3.4 Spatio-Temporal Extension （CoST*）
6.4 Density Queries for Moving Objects in Spatial Networks
6.4.1 ProblemDefinition
6.4.2 Cluster-Based Query Preprocessing
6.4.3Density Query Processing
6.5 Continuous Density Queries for Moving Objects
6.5.1 Problem Definition
6.5.2Building the Quad-Tree
6.5.3 Safe Interval Computation
6.5.4Query Processing
6.6Summary
References
7 Trajectory Prediction of Moving Objects
7.1Introduction
7.2 Underlying Linear 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
7.3.2Time-SegmentedPrediction
7.4Uncertain Path Prediction Methods
7.4.1Preliminary
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
7.6Summary
References
8 Uncertainty Managementin Moving Objects Database
8.1Introduction
8.2Representative Models
8.2.12D-EllipseModel
8.2.23D-CylinderModel
8.2.3 Model the Uncertainty in Database
8.3 Uncertain Trajectory Management
8.3.1 Uncertain Trajectory Modeling
8.3.2Database Operations for Uncertainty Management
8.4Summary
References
9Statistical Analysis on Moving Object Trajectories
9.1Introduction
9.2Representative Methods
9.2.1Based on FCDs
9.2.2Based on MODs
9.3 Real-Time Traffic Analysis on Dynamic Transportation Networks
9.3.1 Modeling Dynamic Transportation Networks
9.3.2Real-Time Statistical Analysis of Traffic Parameters
9.4Summary
References
10 Clustering Analysis of Moving Objects
10.1 Introduction
10.2 Underlying Clustering Analysis Methods
10.3 Clustering Static Objects in Spatial Networks
10.3.1ProblemDefinition
10.3.2Edge-Based Clustering Algorithm
10.3.3 Node-BasedClustering Algorithm
10.4 Clustering Moving Objects in Spatial Networks
10.4.1 CMON Framework
10.4.2Construction and Maintenance of CBs
10.4.3CMON Construction with Different Criteria
10.5 Clustering Trajectories Based on Partition-and-Group
10.5.1 Partition-and-Group Framework
10.5.2Region-Based Cluster
10.5.3 Trajectory-Based Cluster
10.6 Clustering Trajectories Based 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 Typical Dynamic Transport.ation Navigation Strategies
11.2.1 D*Algorithm
11.2.2 Hierarchy Aggregation Tree Based Navigation
11.3 Incremental Route SearchStrategy
11.3.1Problem Definitions
11.3.2 Pre-computation
11.3.3Top-KIntermediate Destinations
11.3.4Route Search and Update
11.4Summary
References
12 LocationPrivacy
12.1Introduction
12.2Privacy Threatsin LBS
12.3 System Architecture
12.3.1 Non-cooperative Architecture
12.3.2Centralized Architecture
12.3.3Peer-to-Peer Architecture
12.4Location Anonymization Techniques
12.4.1Location K-Anonymity Model
12.4.2 p-Sensitivity Model
12.4.3 Anonymization Algorithms
12.5 Evaluation Metrics
12.6Summary
References
Index