图结构能够描述数据对象之间的复杂关系，因而被广泛应用于多种领域。随着相关应用领域的发展，图数据库的规模变得庞大且仍在不断增长。这给研究者在图查询和图挖掘方面带来新的挑战。本文主要研究以下三个问题：如何确定两个图的顶点对应关系，使得其中一个图的子结构匹配到另一个图的相似子结构；如何从含有多个小图的数据库中，找到与查询图相似的图；如何在由不同类别的图组成的数据库中，选取特征子图并对图进行分类。 / 在本文中，对于第一个问题，我们提出了新的两段式图匹配算法。在第一阶段，我们采用了一个新的启发式策略，能够先选取锚顶点并向外扩展，进而快速得到初始匹配。在第二阶段，我们设计了新的算法对初始匹配加以改进，并且证明了新的匹配优于初始匹配。这个两段式图匹配算法能够快速有效地获得两个图的高质量匹配。为解决第二个问题，我们首先定义一个新的度量以衡量两图间的距离。它基于两图间的最大公共子图，能够很好地捕捉两个图的相同及不同之处。由于最大公共子图的计算是NP完全问题，为了快速回答top-k相似图查询，我们提出了一个高效算法，能够极大地减少最大公共子图的计算次数。这个算法根据距离度量的三种下界进行剪枝以筛选掉不合格的图。其中，前两种下界的计算基于两图的结构信息，第三种下界可由距离度量的三角不等式性质推出。我们还设计了三种不同的索引结构来支持剪枝，它们能够在剪枝效果和索引时间方面达到不同程度的平衡。关于第三个问题，我们发现了目前广泛使用的特征判别函数的两个主要缺陷，并据此提出了一个新的多样性特征判别函数。它不仅能衡量特征的判别性，而且能衡量特征的多样性。我们从多个方面分析了这个函数的性质，发现它能更好地区分不同类别的图。基于这个函数，我们设计了新的特征选取算法，获得很高的分类精度。 / Graph has powerful ability to model complex structural relationships among data objects and has been widely used in various applications. Along with the development of the application domains, graph databases become large and are growing rapidly in size. This brings researchers new challenges on graph query and mining, among which we mainly focus on investigating the following three problems: how to find the correspondence between the nodes of two large graphs so that some substructures in one graph are mapped to similar substructures in the other; another problem is how to retrieve similar graphs for a query graph from a graph database consisting of a large number of graphs; and the last problem is how to extract subgraph features to build an automated classification model for a graph database containing graphs which belong to different classes. / In this thesis, for the first problem, we propose a novel two-step approach which can efficiently match two large graphs over thousands of nodes with high matching quality. In the first stage, we design an anchor-selection/expansion scheme to construct a good initial matching heuristically. In the second stage, we propose a new approach to refine the initial matching and give the optimality of our refinement algorithm. Our approach can produce an approximate matching result with high quality and efficiency. To address the second problem, we introduce a new graph distance measure based on the maximum common subgraphs (MCS) of two graphs which can thoroughly capture the common as well as different structures of two graphs. Since computing the MCS of two graphs is NP-complete, to answer the top-k graph similarity query efficiently, we propose a fast algorithm which can significantly reduce the number of MCS computations. This algorithm prunes the unqualified graphs based on three lower bounds in which the first two are derived based on the structures of two graphs and the third is obtained based on the triangle property of the distance measure. Three index schemes are designed with different tradeoffs between pruning power and construction cost to assist the query processing. For the third problem, we identify two main issues of the current widely-used discriminative score for feature selection, and introduce a new diversified discriminative score to explore the additional value of the diversity together with the discriminativity. We analyze the properties of the newly-proposed diversified discriminative score from several perspectives and demonstrate that this score can make positive/negative graphs more separable. New algorithms are also proposed to select features based on the new score and they are shown to have high classification accuracy. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhu, Yuanyuan. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 137-146). / Abstract also in Chinese. / Abstract --- p.i / Abstract in Chinese --- p.iii / Acknowledgments --- p.iv / Contents --- p.vi / List of Tables --- p.x / List of Figures --- p.xi / Notations --- p.1 / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Motivation --- p.2 / Chapter 1.1.1. --- Large Graph Matching --- p.3 / Chapter 1.1.2. --- Top-k Graph Similarity Query --- p.4 / Chapter 1.1.3. --- Diversified Discriminative Feature Selection --- p.6 / Chapter 1.2. --- Contribution --- p.7 / Chapter 2. --- Preliminaries --- p.10 / Chapter 3. --- Related Work --- p.16 / Chapter 3.1. --- Graph Matching --- p.16 / Chapter 3.1.1. --- Exact Graph Matching --- p.16 / Chapter 3.1.2. --- Approximate Graph Matching --- p.17 / Chapter 3.2. --- Graph Similarity Query --- p.19 / Chapter 3.3. --- Graph Classification --- p.20 / Chapter 4. --- Large Graph Matching --- p.23 / Chapter 4.1. --- Problem Statement --- p.23 / Chapter 4.2. --- An Overview: Construction and Refinement --- p.24 / Chapter 4.3. --- Matching Construction --- p.26 / Chapter 4.3.1. --- Global and Local Node Similarity --- p.26 / Chapter 4.3.2. --- Anchor Selection and Expansion --- p.33 / Chapter 4.3.3. --- Discussion on τ for Anchor Selection --- p.36 / Chapter 4.4. --- Matching Refinement --- p.39 / Chapter 4.4.1. --- Vertex Cover Based Refinement --- p.39 / Chapter 4.4.2. --- Refinement and Its Optimality --- p.41 / Chapter 4.4.3. --- Randomly Refinement Excluding C - F₁ --- p.46 / Chapter 4.4.4. --- Randomly Refinement Including C - F₁ --- p.51 / Chapter 4.5. --- Labeled Graph Handling --- p.54 / Chapter 4.6. --- Experiments --- p.56 / Chapter 4.6.1. --- Comparison with the Approximate Algorithms --- p.59 / Chapter 4.6.2. --- Comparison with the Exact Algorithm --- p.63 / Chapter 4.6.3. --- Parameter and Scalability Testing --- p.65 / Chapter 4.6.4. --- Sensitivity of Randomness (PN) --- p.69 / Chapter 4.6.5. --- Effectiveness of Label Distribution --- p.70 / Chapter 4.7. --- Summary --- p.72 / Chapter 5. --- Top-k Graph Similarity Query --- p.73 / Chapter 5.1. --- Problem Statement --- p.73 / Chapter 5.2. --- The Framework --- p.78 / Chapter 5.3. --- Pruning without Indexing --- p.80 / Chapter 5.3.1. --- Edge Frequency Based Lower Bound --- p.80 / Chapter 5.3.2. --- Adjacency List Based Lower Bound --- p.82 / Chapter 5.3.3. --- Query Processing --- p.84 / Chapter 5.4. --- Pruning with Indexing --- p.85 / Chapter 5.4.1. --- The Triangle Property of Graph Distance --- p.86 / Chapter 5.4.2. --- Query Processing --- p.88 / Chapter 5.4.3. --- Indexing --- p.92 / Chapter 5.4.4. --- Discussion on the Generality of Our Framework --- p.94 / Chapter 5.5. --- Experiments --- p.94 / Chapter 5.5.1. --- Similarity Measures Evaluation --- p.96 / Chapter 5.5.2. --- Query Performance Evaluation --- p.98 / Chapter 5.5.3. --- Indexing Cost Evaluation --- p.102 / Chapter 5.6. --- Summary --- p.103 / Chapter 6. --- Diversified Discriminative Feature Selection --- p.105 / Chapter 6.1. --- Problem Statement --- p.105 / Chapter 6.2. --- Discriminative Score --- p.108 / Chapter 6.2.1. --- The Single Feature Discriminative Score --- p.109 / Chapter 6.2.2. --- A New Diversified Discriminative Score --- p.110 / Chapter 6.3. --- Property Statistics of Discriminative Score --- p.113 / Chapter 6.4. --- The Algorithms --- p.117 / Chapter 6.5. --- Ensemble D&D --- p.121 / Chapter 6.6. --- Experiments --- p.123 / Chapter 6.6.1. --- D&D Performance Analysis --- p.126 / Chapter 6.6.2. --- Comparison with Existing Algorithms --- p.127 / Chapter 6.6.3. --- Performance on Patterns Mined by GAIA --- p.129 / Chapter 6.7. --- Summary --- p.131 / Chapter 7. --- Conclusion and FutureWork --- p.132 / Chapter 7.1. --- Conclusion --- p.132 / Chapter 7.2. --- Future work --- p.134 / Bibliography --- p.136

由于现今在各个领域涌现的图数据规模都愈加庞大，在这些大规模图数据上进行任何一种简单的查询都成为一件有富有挑战性的工作。在本文中，我们着重在大规模图上研究三个具有广泛应用的查询：最短路查询，权重限制查询和最近k关键字查询。具体来说， 最短路查询是一个计算两点间最短距离的基本查询。而权重限制查询判断两点间是否存在一条沿路边权都满足用指定条件的可行路径。对于一个查询节点，最近k关键字查询返回k个距离最近的带有指定关键字的节点。在面对一个拥有超过一亿节点的图时，我们需要为这些查询开发有效的索引和查询优化算法。 / 在本文中，对于最短路查询，我们提出了两个基于地标嵌入的算法，一个是有误差控制的地标嵌入算法，另一个则是本地化地标嵌入算法。前者通过对地标的筛选和组织，能对估计的最短距离给予一定的误差保证； 而后者提出的本地化机制能够在不增加预处理复杂度和在线查询复杂度的情况下大幅度提高估计的精准度。对于权重限制查询，我们先提出一个能够保证常数查询时间的内存算法。除此之外，为了提高算法对大规模数据的处理能力，我们使用编码技术设计了一个有效的外存算法。对于最近k关键字查询，我们先在一个特殊的图，即一颗树上，开发一个有效算法来在常数时间内回答最近k关键字查询， 并由此得出一个图上的近似算法；此外我们还通过一个全局存储的技术来进一步减少索引大小和缩短查询时间。我们在真实和模拟的数据上做了大量的实验，实验结果证明我们的算法在大图上对上述三个查询都具有高效性能。 / Due to the massive size of graphs from various domains nowadays, even simple graph queries become challenging tasks. In this thesis, three queries with a wide range of applications are investigated on large graphs. One is shortest distance query, a fundamental query which computes the shortest distance between two nodes. Another query, weight constraint reachability (WCR), checks if there is a feasible path between two nodes where edge weights along the path satisfy a side constraint. And the third one, a top-k nearest keywords (k-NK) query, reports, for a query node, the k nearest nodes bearing some user-specified keywords. When confronting with a large-scale graph with over tens of millions of nodes, we need to develop efficient indexing and query optimization techniques for these queries. / In this thesis, for a shortest distance query, we devise two landmark embedding schemes, an error bounded landmark scheme and a local landmark scheme, where the former can guarantee an error bound for estimated distance, and the latter can significantly improve the distance estimation accuracy without increasing the offline embedding or the online query complexity. For a WCR query, we propose a memorybased approach which promises a constant query time. Besides, in order to increase its scalability, we devise an I/O-efficient approach for answering a WCR query on massive graphs. For a k-NK query, we start with a special case when the graph is a tree, based on which we present our algorithm for approximate k-NK query on a graph. A global storage technique is devised to further reduce the index size and the query time. We did extensive experiments on the three queries respectively to show the effectiveness and efficiency of our methods. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Qiao, Miao. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 141-151). / Abstract also in Chinese. / Abstract --- p.i / Abstract in Chinese --- p.ii / Acknowledgements --- p.iii / Contents --- p.v / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Motivation --- p.1 / Chapter 1.1.1. --- Shortest Distance Query --- p.1 / Chapter 1.1.2. --- Weight Constraint Reachability Query --- p.4 / Chapter 1.1.3. --- Top-k Nearest Keyword Query --- p.7 / Chapter 1.2. --- Contributions --- p.9 / Chapter 1.3. --- Roadmap --- p.11 / Chapter 2. --- RelatedWork --- p.12 / Chapter 2.1. --- Shortest Distance Query --- p.12 / Chapter 2.2. --- Reachability Query --- p.14 / Chapter 2.3. --- Keyword Related Query --- p.15 / Chapter 3. --- Querying Shortest Distance --- p.17 / Chapter 3.1. --- Landmark Embedding --- p.17 / Chapter 3.2. --- Error Bounded Landmark Scheme --- p.18 / Chapter 3.2.1. --- Problem Statement --- p.18 / Chapter 3.2.2. --- Proposed Algorithm --- p.18 / Chapter 3.2.3. --- Graph Partitioning-based Heuristic --- p.22 / Chapter 3.2.4. --- Experiments --- p.27 / Chapter 3.3. --- Query-Dependent Local Landmark Scheme --- p.34 / Chapter 3.3.1. --- Problem Statement --- p.34 / Chapter 3.3.2. --- Shortest Path Tree Based Local Landmark --- p.37 / Chapter 3.3.3. --- Optimization Techniques --- p.41 / Chapter 3.3.4. --- Local Landmark Scheme on Relational Database --- p.48 / Chapter 3.3.5. --- Experiment --- p.56 / Chapter 3.4. --- Summary --- p.64 / Chapter 4. --- QueryingWeight Constraint Reachability --- p.65 / Chapter 4.1. --- Problem Definition --- p.65 / Chapter 4.1.1. --- Edge Weight Constraint --- p.65 / Chapter 4.1.2. --- Node Weight Constraint --- p.66 / Chapter 4.1.3. --- Two Basic Solutions --- p.67 / Chapter 4.2. --- An Efficient Memory Algorithm --- p.68 / Chapter 4.2.1. --- Properties of WCR --- p.68 / Chapter 4.2.2. --- Novel Edge Based Indexing --- p.70 / Chapter 4.2.3. --- Extension to Other Constraint Formats --- p.76 / Chapter 4.3. --- An I/O-Efficient Index --- p.77 / Chapter 4.3.1. --- Vertex Coding --- p.78 / Chapter 4.3.2. --- MST Re-balancing --- p.80 / Chapter 4.3.3. --- Disk-Based Index Construction --- p.84 / Chapter 4.3.4. --- Query Processing --- p.85 / Chapter 4.4. --- Experiments --- p.87 / Chapter 4.5. --- Summary --- p.101 / Chapter 5. --- Querying Top K-Nearest Keyword --- p.102 / Chapter 5.1. --- Problem Definition --- p.102 / Chapter 5.2. --- Existing Solutions --- p.103 / Chapter 5.2.1. --- Approximate k-NK on a Graph --- p.104 / Chapter 5.2.2. --- Exact 1-NK on a Tree --- p.106 / Chapter 5.3. --- Solution Overview --- p.108 / Chapter 5.4. --- K-NK on a Tree for a Small K --- p.110 / Chapter 5.4.1. --- Query Processing --- p.110 / Chapter 5.4.2. --- Construction of Entry Edge Partition --- p.115 / Chapter 5.4.3. --- Construction of Candidate List --- p.118 / Chapter 5.5. --- K-NK on a Tree for a Large K --- p.120 / Chapter 5.5.1. --- A Basic Pivot Approach --- p.121 / Chapter 5.5.2. --- Pivot Approach with Tree Balancing --- p.122 / Chapter 5.5.3. --- Index Construction --- p.125 / Chapter 5.6. --- Approximate K-NK on a Graph --- p.128 / Chapter 5.7. --- Experiments --- p.133 / Chapter 5.8. --- Summary --- p.138 / Chapter 6. --- Conclusions and Future Work --- p.139 / Bibliography --- p.140

Graph-structured data is enjoying an increasing popularity among Web technology and new data management and archiving techniques. Numerous applications work with graphs and need to query reachability among nodes in graphs. A 2-hop cover can compactly represent the whole edge transitive closure of a graph in O ( / Cheng, Jiefeng. / "August 2007." / Adviser: Jeffrey Xu Yu. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1097. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 134-141). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

In this dissertation, we develop frequent free tree mining systems, F3TM and CFFTree, to systematically discover the complete set of (closed) frequent free trees from a graph database. Graph indexing, another problem addressed in this dissertation, is of special interest both in academia and in industrial applications. The solutions, (Tree+Delta ) and SimTree, propose a free tree featured index, which is built on frequent free tree patterns discovered through a structural mining process. This mining-based indexing methodology leads to the development of a compact but effective graph index structure that is orders of magnitude smaller in size but an order of magnitude faster in performance than traditional approaches. / Scalable structural pattern mining and graph database management tools become increasingly crucial to applications with complex data in domains ranging from software engineering to computational biology. Due to their high complexity, it is often difficult, if not impossible, for human beings to manually analyze any reasonably large collection of graphs. In this dissertation, we investigate two fundamental problems in large scale graph databases: Given a graph database what are the hidden free tree patterns and how can we find them? And how can we index graphs based on free tree patterns and perform similarity search in large graph databases? / The developed concepts, theories, and systems hence increase our understanding of data mining principles in structural pattern discovery, interpretation and search. The formulation of a general free tree featured graph information system through this study could provide fundamental supports to graph-intensive applications. / Zhao, Peixiang. / "August 2007." / Adviser: Jeffrey Xu Yu. / Source: Dissertation Abstracts International, Volume: 69-02, Section: B, page: 1127. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 136-145). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

by Cheung King Lum Kingly. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 108-110). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Problems in Content-Based Indexing --- p.2 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- Content-Based Indexing Structures --- p.5 / Chapter 2.1 --- R-Tree --- p.6 / Chapter 2.2 --- R+-Tree --- p.8 / Chapter 2.3 --- R*-Tree --- p.11 / Chapter 3 --- Searching in Both R-Tree and R*-Tree --- p.15 / Chapter 3.1 --- Exact Search --- p.15 / Chapter 3.2 --- Nearest Neighbor Search --- p.19 / Chapter 3.2.1 --- Definition of Searching Metrics --- p.19 / Chapter 3.2.2 --- Pruning Heuristics --- p.21 / Chapter 3.2.3 --- Nearest Neighbor Search Algorithm --- p.24 / Chapter 3.2.4 --- Generalization to N-Nearest Neighbor Search --- p.25 / Chapter 4 --- An Improved Nearest Neighbor Search Algorithm for R-Tree --- p.29 / Chapter 4.1 --- Introduction --- p.29 / Chapter 4.2 --- New Pruning Heuristics --- p.31 / Chapter 4.3 --- An Improved Nearest Neighbor Search Algorithm --- p.34 / Chapter 4.4 --- Replacing Heuristics --- p.36 / Chapter 4.5 --- N-Nearest Neighbor Search --- p.41 / Chapter 4.6 --- Performance Evaluation --- p.45 / Chapter 5 --- Overlapping Nodes in R-Tree and R*-Tree --- p.53 / Chapter 5.1 --- Overlapping Nodes --- p.54 / Chapter 5.2 --- Problem Induced By Overlapping Nodes --- p.57 / Chapter 5.2.1 --- Backtracking --- p.57 / Chapter 5.2.2 --- Inefficient Exact Search --- p.57 / Chapter 5.2.3 --- Inefficient Nearest Neighbor Search --- p.60 / Chapter 6 --- Redundancy On R-Tree --- p.64 / Chapter 6.1 --- Motivation --- p.64 / Chapter 6.2 --- Adding Redundancy on Index Tree --- p.65 / Chapter 6.3 --- R-Tree with Redundancy --- p.66 / Chapter 6.3.1 --- Previous Models of R-Tree with Redundancy --- p.66 / Chapter 6.3.2 --- Redundant R-Tree --- p.70 / Chapter 6.3.3 --- Level List --- p.71 / Chapter 6.3.4 --- Inserting Redundancy to R-Tree --- p.72 / Chapter 6.3.5 --- Properties of Redundant R-Tree --- p.77 / Chapter 7 --- Searching in Redundant R-Tree --- p.82 / Chapter 7.1 --- Exact Search --- p.82 / Chapter 7.2 --- Nearest Neighbor Search --- p.86 / Chapter 7.3 --- Avoidance of Multiple Accesses --- p.89 / Chapter 8 --- Experiment --- p.90 / Chapter 8.1 --- Experimental Setup --- p.90 / Chapter 8.2 --- Exact Search --- p.91 / Chapter 8.2.1 --- Clustered Data --- p.91 / Chapter 8.2.2 --- Real Data --- p.93 / Chapter 8.3 --- Nearest Neighbor Search --- p.95 / Chapter 8.3.1 --- Clustered Data --- p.95 / Chapter 8.3.2 --- Uniform Data --- p.98 / Chapter 8.3.3 --- Real Data --- p.100 / Chapter 8.4 --- Discussion --- p.102 / Chapter 9 --- Conclusions and Future Research --- p.105 / Chapter 9.1 --- Conclusions --- p.105 / Chapter 9.2 --- Future Research --- p.106 / Bibliography --- p.108

by Edward Nai Biu Tam. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 92-93). / Abstract --- p.ii / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problems and Definitions --- p.1 / Chapter 1.1.1 --- Spatial Data --- p.1 / Chapter 1.1.2 --- Spatial Queries --- p.2 / Chapter 1.1.3 --- Spatial Access Method --- p.3 / Chapter 1.1.4 --- Motivations --- p.4 / Chapter 1.2 --- Architecture for Parallel SAM --- p.4 / Chapter 1.2.1 --- Parameters --- p.4 / Chapter 1.3 --- Objectives of the thesis --- p.7 / Chapter 1.4 --- Overview of the thesis --- p.7 / Chapter 2 --- Review of the Literature --- p.9 / Chapter 2.1 --- R-tree --- p.9 / Chapter 2.1.1 --- Structure --- p.9 / Chapter 2.1.2 --- Searching --- p.10 / Chapter 2.1.3 --- Searching Costs --- p.11 / Chapter 2.1.4 --- Insertion --- p.14 / Chapter 2.1.5 --- Deletion --- p.15 / Chapter 2.1.6 --- Splitting Algorithm --- p.16 / Chapter 2.2 --- Independent R-tree --- p.19 / Chapter 2.2.1 --- Data Distribution --- p.19 / Chapter 2.2.2 --- Space Partition --- p.19 / Chapter 2.3 --- Super Node --- p.19 / Chapter 2.4 --- Multiplexed R-tree --- p.20 / Chapter 2.4.1 --- Node Allocation Algorithm --- p.21 / Chapter 3 --- Comparison of Different Parallel R-tree Methods --- p.23 / Chapter 3.1 --- Throughput --- p.24 / Chapter 3.2 --- Service Time --- p.32 / Chapter 3.3 --- Summary --- p.37 / Chapter 3.3.1 --- Data Distribution --- p.37 / Chapter 3.3.2 --- Space Partition --- p.37 / Chapter 3.3.3 --- Multiplexed R-trees --- p.37 / Chapter 3.3.4 --- Super-nodes --- p.37 / Chapter 3.3.5 --- Conclusions --- p.38 / Chapter 4 --- Parallel Binary R-tree --- p.39 / Chapter 4.1 --- Architecture --- p.39 / Chapter 4.1.1 --- Extension of PBR-tree --- p.41 / Chapter 4.2 --- Searching Algorithm --- p.41 / Chapter 4.2.1 --- Asynchronous --- p.41 / Chapter 4.2.2 --- Synchronous --- p.45 / Chapter 4.3 --- Performance Evaluation --- p.45 / Chapter 4.3.1 --- Asynchronous --- p.49 / Chapter 4.3.2 --- Synchronous --- p.49 / Chapter 4.3.3 --- Comparison of the Cost models --- p.52 / Chapter 4.4 --- Application of PBR-tree --- p.54 / Chapter 4.4.1 --- Experimental Setup --- p.54 / Chapter 4.4.2 --- Experimental results --- p.55 / Chapter 4.5 --- Conclusions --- p.56 / Chapter 5 --- Multiplexed Parallel R-trees --- p.60 / Chapter 5.1 --- Architecture --- p.60 / Chapter 5.2 --- Searching --- p.61 / Chapter 5.3 --- Node Allocation Algorithm --- p.61 / Chapter 6 --- Comparisons and Experiment --- p.63 / Chapter 6.1 --- Assumptions and Experimental Setup --- p.63 / Chapter 6.2 --- Experimental Results --- p.65 / Chapter 6.2.1 --- Waiting time --- p.65 / Chapter 6.2.2 --- Service time --- p.65 / Chapter 6.3 --- Experimental results for large number of processors --- p.74 / Chapter 7 --- Conclusions --- p.83 / Chapter 7.1 --- Summary --- p.83 / Chapter 7.1.1 --- Studies on the previous works --- p.83 / Chapter 7.1.2 --- Parallel Binary R-tree --- p.84 / Chapter 7.1.3 --- Multiplexed Parallel R-trees --- p.84 / Chapter 7.2 --- Future Works --- p.84 / Chapter A --- Proximity Measure --- p.86 / Chapter B --- Summary of Variables and Symbols --- p.88 / Chapter C --- Proof of Equations --- p.90 / Bibliography --- p.92

by Fung Tze Wa. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 92-96). / Abstracts in English and Chinese. / Abstract in English --- p.i / Abstract in Chinese --- p.ii / Acknowledgements --- p.iii / Table of Contents --- p.iv-vi / List of Figures --- p.vii-ix / List of Tables --- p.x / Chapter Chapter 1 --- Introduction / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.3 --- Purposes of this Research --- p.6 / Chapter 1.4 --- Contribution of this Research --- p.7 / Chapter 1.5 --- Outline of the Thesis --- p.9 / Chapter Chapter 2 --- Literature Review And Research Issues / Chapter 2.1 --- Introduction --- p.11 / Chapter 2.2 --- Spatial Access Methods --- p.14 / Chapter 2.2.1 --- R-Tree --- p.15 / Chapter 2.2.2 --- R*-Tree --- p.19 / Chapter 2.2.3 --- R+-Tree --- p.21 / Chapter 2.3 --- Spatial Network Analysis --- p.22 / Chapter 2.4 --- Nearest Neighbor Queries --- p.23 / Chapter 2.5 --- Summary --- p.25 / Chapter Chapter 3 --- Data Preparation / Chapter 3.1 --- "Introduction (XML, GML), XML indexing" --- p.26 / Chapter 3.2 --- Spatial data from Lands Department --- p.31 / Chapter 3.3 --- Graph representation for Road Network data --- p.32 / Chapter 3.4 --- Summary --- p.35 / Chapter Chapter 4 --- XML Indexing for Spatial Data / Chapter 4.1 --- Introduction --- p.36 / Chapter 4.2 --- STR Packed R-Tree --- p.38 / Chapter 4.2.1 --- Implementation --- p.39 / Chapter 4.2.2 --- Experimental Result --- p.41 / Chapter 4.3 --- Summary --- p.48 / Chapter Chapter 5 --- Spatial Network / Chapter 5.1 --- Introduction --- p.50 / Chapter 5.2 --- CCAM: Connectivity-Clustered Access Method --- p.53 / Chapter 5.3 --- Shortest Path in Spatial Network --- p.56 / Chapter 5.4 --- A New Algorithm Specially for Partitioning /Clustering Network --- p.63 / Chapter 5.5 --- A New Simple heuristic for Shortest Path Problem for Spatial Network --- p.70 / Chapter 5.6 --- Summary --- p.74 / Chapter Chapter 6 --- Nearest Neighbor Queries / Chapter 6.1 --- Introduction --- p.76 / Chapter 6.2 --- Modified Algorithm for Nearest Neighbor Queries --- p.78 / Chapter 6.3 --- Summary --- p.83 / Chapter Chapter 7 --- Conclusion and Future Work / Chapter 7.1 --- Conclusion --- p.84 / Chapter 7.2 --- Future Work --- p.85 / Appendix Space Driven Algorithm / Chapter A.1 --- Introduction --- p.87 / Chapter A.2 --- Fixed Grid --- p.88 / Chapter A.3 --- Z-curve --- p.89 / Chapter A.4 --- Hilbert curve --- p.90 / Chapter A.5 --- Conclusion --- p.91 / Bibliography --- p.92

Thesis. 1979. Ph.D.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Vita. / Bibliography: p. 241-248. / Ph.D.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: p. 110-111. / by Rowland Frank Archer, Jr. / M.S.

Thesis (M.S. in computer science)--Washington State University, December 2007. / Includes bibliographical references (p. 77-79).