移动对象管理 模型、技术与应用 第2版 英文版
作者：孟小峰，丁治明，许佳捷 著
出版时间：2014年版
内容简介
　　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。这本由孟小峰、丁治明、许佳捷著的《移动对象管理（模型技术与应用第2版英文版）（精）》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。
目录
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