由於無線通訊技術、定位技術和行動運算快速的發展，行動服務對於大型時間空間資料庫的管理變的實際且重要。行動服務的使用者只能夠在空間網絡(道路網絡)移動。他們通常發出K 個最近鄰居(KNN)的查詢來得到在道路網絡中可以到達的資料對象。對於行動服務而言，如何有效率的回答所對應使用者有興趣的資料對象是一個挑戰的問題。因此，如何有效率的模組道路網絡、有效率的索引方式及執行查詢在道路網絡中已經成為一個熱門的主題。Lu 學者提出了一個道路網絡模組的方法，它記錄真實生活中的道路網絡，這個方法比其他模組方法都還要好。然後，基於他們的模組，他們提出一個道路網絡
網格Road Network Grid (RNG)的索引方法來加速在真實道路網絡中K 個最近鄰居查詢的速度。RNG 索引結構是一個四元樹(quad-tree)的結構，並且它是一個以點為基礎的索引結構。然而，在他們的模組中，他們將雙向且某些位置是可以迴轉的道路做分割。這樣的模組方式並不能精準的表達真實道路的情況。由於他們將道路做分割，造成了圖中的點的數量增加。這也造成圖形需要分割的次數增加。造成建立索引結構的時間增加。而他們的葉節點格式也造成搜尋時間增加。並且在RNG 的索引結構中執行最近K 個鄰居的查詢必須拜訪根節點許多次。這樣的情形造成搜尋的時間增加。因此，在這篇論文，我們提出一個道路網絡的模組來記錄真實道路網絡。當我們在將實際的道路網絡對映成圖的時候，我們無須去切割真實道路。我們將道路網路直接的對映成圖。然後，基於我們的模組方法，我們提出一個以邊為基礎的九方區域Edge-Based Nine Area tree(EBNA)的索引方式來加速獲得有興趣邊的資訊的尋找時間。EBNA 索引結構是一個以邊為基礎的索引結構。我們將邊的資訊都儲存在葉節點中。每一個邊的資訊項目中都有一個指標指向相連接的邊。這些連結形成一個圖。這個圖使得K 個最近鄰居的查詢只需拜訪根節點一次。從我們的實驗數據，我們看到在建立EBNA 索引的效率比建立RNG 索引的效率要好並且在EBNA 索引上執行K 個最近鄰居查詢的效率比在RNG 索引上執行K 個最近鄰居查詢的效率好。

Due to wireless communication technologies, positioning technologies, and mobile computing develop quickly, mobile services are becoming practical and important on
big spatiotemporal databases management. Mobile service users move only inside a spatial network, e.g. a road network. They often issue the K Nearest Neighbor (KNN) query to obtain data objects reachable through the road network. The challenge problem of mobile services is how to efficiently answer the data objects which user interest to the corresponding mobile users. Therefore, how to effectively modeling road networks, effectively indexing, and querying on the road networks have become a popular topic. Lu et. al. have proposed a road network model that captures the real-life road networks better than previous models. Then, based on their model, they have proposed a RNG (Road Network Grid) index for speeding up the KNN
query on real-life road networks. The RNG index structure is a quad-tree structure and a point-based structure. However, in their model, they divide the double track road which U-turn is allowed at some parts. This modeling does not capture the real-life road networks accurately. Since they divide the road, this makes the number of points of the graph increase. The number of times of partitioning the graph increases. It increases the execution time of constructing the index structure. The format of the leaf node of the RNG index makes the search time increase. Moreover, the query processing on the RNG index structure has to visit the root repeatedly. This condition makes the search time increase. Therefore, in this thesis, we propose a network model that captures the real-life road networks. We do not have to divide the real-life roads when we map the real-life roads into graph. We map the real-life road networks into graph directly. Then, based on our network model, we propose an EBNA (Edge-Based Nine-Area tree) index structure to make the search time of obtaining the interest edge information quickly. The EBNA index structure is an edge-based index structure. We store all of the edge information on the leaf node. We can obtain the edge information directly. Each edge information entry has a
pointer point to link edges. Links of each edge entry consist a graph. This graph makes the KNN query processing visit the root only one time. From our simulation result, we show that the performance of constructing the EBNA index is better than constructing the RNG index and the performance of the KNN query processing by using EBNA index is better than the KNN query processing by using RNG index.

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Spatial Network Databases . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Query Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Indexing and Query Processing in Spatial Network Databases . . . . 5
1.4 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 11
2. A Survey of Query Processing in Spatial Network Database . . . 12
2.1 The Incremental Network Expansion Approach . . . . . . . . . . . . 12
2.2 The S-GRID Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Grid Partition and Pre-computation . . . . . . . . . . . . . . 14
2.2.2 KNN and CKNN Query Processing . . . . . . . . . . . . . . . 16
2.3 The Distance Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 The Network Model . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.2 Index Structure and KNN Algorithm . . . . . . . . . . . . . . 19
2.4 The RNG Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Modeling the Real-Life Network . . . . . . . . . . . . . . . . . 21
2.4.2 Indexing and Query Processing . . . . . . . . . . . . . . . . . 23
3. Edge-Based Query Processing in Real-Life Road Networks . . . . 26
3.1 Modeling the Real-Life Road Network . . . . . . . . . . . . . . . . . . 26
3.2 The EBNA Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 The Partition Numbering Scheme . . . . . . . . . . . . . . . . 31
3.2.2 The NA-Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2.3 The EBNA Index Structure . . . . . . . . . . . . . . . . . . . 37
3.3 K Nearest Neighbor Query Processing . . . . . . . . . . . . . . . . . 44
3.4 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4. Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.1 The Performance Model . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.2 Simulation Results of the Index Structure . . . . . . . . . . . . . . . 60
4.3 Simulation Results of KNN Queries . . . . . . . . . . . . . . . . . . . 65
5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
BIBLIOGRAPHY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

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