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书名 移动对象管理(模型技术与应用)(精)
分类 科学技术-工业科技-电子通讯
作者 孟小峰//陈继东
出版社 清华大学出版社
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简介
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本书总结了国内外有关移动数据管理的研究工作和具有代表性的关键技术,并较详细地介绍了作者近年来的一些研究成果,具体内容涉及:移动对象管理模型,移动对象管理技术,以及移动对象管理应用。具有较大的参考价值。本书为全英文版本。

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随着移动通信技术的不断发展和普及,人们对移动对象管理的需求越来越迫切。移动对象管理成为数据库研究领域的一个热门方向,它在许多领域都展现了广阔的应用前景。本书比较系统地介绍了移动对象管理的相关内容,即移动对象管理模型(包括移动对象建模、移动对象更新、移动对象索引等),移动对象管理技术(包括移动对象查询、移动对象预测、移动数据不确定性研究等),移动对象管理应用(包括动态交通导航、动态交通网络、移动对象聚类分析、位置隐私保护等)。

本书总结了国内外有关移动数据管理的研究工作和具有代表性的关键技术,并较详细地介绍了作者近年来的一些研究成果,具有较大的参考价值。

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

Part I Moving Objects Management Models

 1 Introduction

1.1 Background

 1.1.1 Mobile Computing

 1.1.2 Positioning Techniques

1.2 Location-Based Services

1.3 Mobile Data Management

1.4 Moving Object Databases

References

 2 Moving Objects Modeling

2.1 Introduction

2.2 Underlying Models

2.3 Graphs of Cellular Automata Model

 2.3.1 Cellular Automata (CA)

 2.3.2 Structure of GCA

 2.3.3 Trajectory of GCA

 2.3.4 Transition of GCA

 2.3.5 Two-Lane GCA

2.4 Summary

References

 3 Moving Objects Updating

3.1 Introduction

3.2 Underlying Update Strategies

 3.2.1 Based on Threshold

 3.2.2 Based on Location Prediction

 3.2.3 Based on Object Grouping

3.3 Proactive Location Update Strategy

3.4 Group Location Update Strategy

3.5 Summary

References

 4 Moving Objects Indexing

4.1 Introduction

4.2 Underlying Indexing Structures

 4.2.1 The R-Tree

 4.2.2 The Grid File

 4.2.3 The Quad-Tree

4.3 Indexing Moving Objects in Euclidean Space

 4.3.1 The R-Tree-Based Index

 4.3.2 The Grid-Based Index

 4.3.3 The Quad-Tree-Based Index

4.4 Indexing Moving Objects in Spatial Networks

 4.4.1 The Adaptive Unit

 4.4.2 The Adaptive Network R-Tree (ANR-Tree)

4.5 Indexing Past, Present, and Future Trajectories

 4.5.1 Indexing Future Trajectory

 4.5.2 Indexing History Trajectories

4.6 Update-Efficient Indexing Structures

4.7 Summary

References

Part II Moving Objects Management Techniques

 5 Moving Objects Basic Querying

5.1 Introduction

5.2 Classifications of Moving Object Queries

 5.2.1 Based on Spatial Predicates

 5.2.2 Based on Temporal Predicates

 5.2.3 Based on Moving Spaces

5.3 NN Queries

 5.3.1 Incremental Euclidean Restriction

 5.3.2 Incremental Network Expansion

5.4 Range Queries

 5.4.1 Range Euclidean Restriction

 5.4.2 Range Network Expansion

5.5 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 Density Queries for Moving Objects in Spatial Networks

 6.3.1 Problem Definition

 6.3.2 Cluster-Based Query Preprocessing

 6.3.3 Density Query Processing

6.4 Continuous Density Queries for Moving Objects

 6.4.1 Problem Definition

 6.4.2 Building the Quad-Tree

 6.4.3 Safe Interval Computation

 6.4.4 Query Processing

6.5 Summary

References

 7 Trajectory Prediction of Moving Objects

7.1 Introduction

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

 7.3.2 Time-Segmented Prediction

7.4 Other Non-Linear Prediction Methods

7.5 Summary

References

 8 Uncertainty of Moving Objects

8.1 Introduction

8.2 Uncertain Trajectory Modeling

8.3 Uncertain Trajectory Indexing

 8.3.1 Structure of the UTR-Tree

 8.3.2 Construction and Maintenance of UTR-Tree

8.4 Uncertainty Trajectory Querying

8.5 Summary

References

Part III Moving Objects Management Applications

 9 Dynamic Transportation Navigation

9.1 Introduction

9.2 Moving Objects Management Application Scenarios

9.3 Dynamic Transportation Navigation

 9.3.1 Hierarchy Aggregation Tree

 9.3.2 Dynamic Navigation Query Processing

 9.3.3 Dynamic Navigation System Architecture

9.4 Summary

References

 10 Dynamic Transportation Networks

10.1 Introduction

10.2 The System Architecture

10.3 Data Model of Transportation Network and Moving Objects

10.4 Querying Moving Objects in Transportation Networks

 10.4.1 Computing the Locations Through Interpolation

 10.4.2 Querying Moving Objects with Uncertainty

 10.4.3 Location Prediction in Transportation Networks

10.5 Summary

References

 11 Clustering Analysis of Moving Objects

11.1 Introduction

11.2 Underlying Clustering Analysis Methods

11.3 Clustering Static Objects in Spatial Networks

 11.3.1 Problem Definition

 11.3.2 Edge-Based Clustering Algorithm

 11.3.3 Node-Based Clustering Algorithm

11.4 Clustering Moving Objects in Spatial Networks

 11.4.1 CMON Framework

 11.4.2 Construction and Maintenance of CBs

 11.4.3 CMON Construction with Different Criteria

11.5 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 Anonyrnization 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

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