移动对象管理

出版时间：2010-5  出版社：清华大学出版社  作者：孟小峰，陈继东 著  页数：190  

内容概要

　　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。《移动对象管理：模型、技术与应用》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）。　　《移动对象管理：模型、技术与应用》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。　　《移动对象管理：模型、技术与应用》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。

书籍目录

Part I Moving Objects Management ModelsIntroduction1.1 Background1.1.1 Mobile Computing1.1.2 Positioning Techniques1.2 Location-Based Services1.3 Mobile Data Management1.4 Moving Object DatabasesReferencesMoving Objects Modeling2.1 Introduction2.2 Underlying Models2.3 Graphs of Cellular Automata Model2.3.1 Cellular Automata （CA）2.3.2 Structure of GCA2.3.3 Trajectory of GCA2.3.4 Transition of GCA2.3.5 Two-Lane GCA2.4 SummaryReferencesMoving Objects Updating3.1 Introduction3.2 Underlying Update Strategies3.2.1 Based on Threshold3.2.2 Based on Location Prediction3.2.3 Based on Object Grouping3.3 Proactive Location Update Strategy3.4 Group Location Update Strategy3.5 SummaryReferencesMoving Objects Indexing4.1 Introduction4.2 Underlying Indexing Structures4.2.1 The R-Tree4.2.2 The Grid File4.2.3 The Quad-Tree4.3 Indexing Moving Objects in Euclidean Space4.3.1 The R-Tree-Based Index4.3.2 The Grid-Based Index4.3.3 The Quad-Tree-Based Index4.4 Indexing Moving Objects in Spatial Networks4.4.1 The Adaptive Unit4.4.2 The Adaptive Network R-Tree （ANR-Tree）4.5 Indexing Past, Present, and Future Trajectories4.5.1 Indexing Future Trajectory4.5.2 Indexing History Trajectories4.6 Update-Efficient Indexing Structures4.7 SummaryReferencesPart II Moving Objects Management Techniques5 Moving Objects Basic Querying5.1 Introduction5.2 Classifications of Moving Object Queries5.2.1 Based on Spatial Predicates5.2.2 Based on Temporal Predicates5.2.3 Based on Moving Spaces5.3 NN Queries5.3.1 Incremental Euclidean Restriction5.3.2 Incremental Network Expansion5.4 Range Queries5.4.1 Range Euclidean Restriction5.4.2 Range Network Expansion5.5 SummaryReferencesMoving Objects Advanced Querying6.1 Introduction6.2 Similar Trajectory Queries for Moving Objects6.2.1 Problem Definition6.2.2 Trajectory Similarity6.2.3 Query Processing6.3 Density Queries for Moving Objects in Spatial Networks6.3.1 Problem Definition6.3.2 Cluster-Based Query Preprocessing6.3.3 Density Query Processing6.4 Continuous Density Queries for Moving Objects6.4.1 Problem Definition6.4.2 Building the Quad-Tree6.4.3 Safe Interval Computation6.4.4 Query Processing6.5 SummaryReferences ..Trajectory Prediction of Moving Objects7.1 Introduction7.2 Underlying Linear Prediction （LP） Methods7.2.1 General Linear Prediction7.2.2 Road Segment-Based Linear Prediction ..7.2.3 Route-Based Linear Prediction7.3 Simulation-Based Prediction （SP） Methods7.3.1 Fast-Slow Bounds Prediction7.3.2 Time-Segmented Prediction7.4 Other Non-Linear Prediction Methods7.5 SummaryReferencesUncertainty of Moving Objects8.1 Introduction8.2 Uncertain Trajectory Modeling8.3 Uncertain Trajectory Indexing8.3.1 Structure of the UTR-Tree8.3.2 Construction and Maintenance of UTR-Tree8.4 Uncertainty Trajectory Querying8.5 SummaryReferencesPart III Moving Objects Management Applications9 Dynamic Transportation Navigation9.1 Introduction9.2 Moving Objects Management Application Scenarios..9.3 Dynamic Transportation Navigation9.3.1 Hierarchy Aggregation Tree9.3.2 Dynamic Navigation Query Processing9.3.3 Dynamic Navigation System Architecture9.4 SummaryReferences10 Dynamic Transportation Networks10.1 Introduction10.2 The System Architecture10.3 Data Model of Transportation Network and Moving Objects...10.4 Querying Moving Objects in Transportation Networks10.4.1 Computing the Locations Through Interpolation10.4.2 Querying Moving Objects with Uncertainty10.4.3 Location Prediction in Transportation Networks10.5 SummaryReferences11 Clustering Analysis of Moving Objects11.1 Introduction11.2 Underlying Clustering Analysis Methods11.3 Clustering Static Objects in Spatial Networks11.3.1 Problem Definition11.3.2 Edge-Based Clustering Algorithm11.3.3 Node-Based Clustering Algorithm11.4 Clustering Moving Objects in Spatial Networks11.4.1 CMON Framework11.4.2 Construction and Maintenance of CBs11.4.3 CMON Construction with Different Criteria11.5 SummaryReferences12 Location Privacy12.1 Introduction12.2 Privacy Threats in LBS12.3 System Architecture12.3.1 Non-Cooperative Architecture12.3.2 Centralized Architecture12.3.3 Peer-to-Peer Architecture12.4 Location Anonyrnization Techniques12.4.1 Location K-Anonymity Model12.4.2 p-Sensitivity Model12.4.3 Anonymization Algorithms12.5 Evaluation Metrics12.6 SummaryReferencesIndex

章节摘录

　　InChapter3,afewunderlyinglocationupdatemethodsareintroducedbasedonthresholds,locationprediction,andobjectgrouping.Then,wedescribetwolocationupdatestrategiesindetail,whichcanimprovetheperformance.Oneistheproactivelocationupdatestrategy,whichpredictsthemovementofmovingobjectsinordertolowertheupdatefrequency;theotheristhegrouplocationupdatestrategy,whichgroupstheobjectstominimizethetotalnumberofobjectsreportingtheirlocations.InChapter4,wefirstintroduceafewoftheunderlyingspatialindexstructuresincludingtheR-tree,GridFile,andQuad-tree.Then,weproposetheindexingmethodsformovingobjectsinEuclideanspaceandinspatialnetworks,respectively.Threeindexingstructures:thetimeparameterizedR-tree（TPR-tree）,GridFile-basedmovingobjectsindex（GMOI）,andfuturetrajectoryQuad-tree（FT-Quad-tree）arepresentedtoimprovetheR-tree,GridFile,andQuad-treeindexstructuresformovingobjectsinEuclideanspace.Formovingobjectsinspatialnetworks,weintroduceadynamicdatastructure,calledadaptiveunitandtheadaptivenet-workR-tree（ANR-tree）tosolvetheindexupdateproblemandtosupportpredictivequeryingofmovingobjects.BynaturallyextendingtheANR-treetoindexhistoricaltrajectory,itcanbeusedtoindexthepast,present,andfuturepositionsofmovingobjectsinroadnetworks.Finally,wediscusshowtoreduceindexupdatesinexistingmovingobjectsindexingstructures.