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研究生:陳慧玲
研究生(外文):Hue-Ling Chen
論文名稱:尋找最近鄰居方法之設計與分析
論文名稱(外文):Design and Analysis of Nearest Neighbor Search Strategies
指導教授:張玉盈
指導教授(外文):Ye-In Chang
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:105
中文關鍵詞:四方區域樹空間曲線空間查詢九方區域樹最近鄰居
外文關鍵詞:nearest neighborNA-treespatial queryquadtreespace-filling curve
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隨著無線通訊及科技的快速進步,面對空間資料庫 (Spatial Database) 資料龐大且複雜的特性,必須要有一套有效率的演算法來回應查詢的問題。空間資料 (spatial data) ,包括二維以上的資料,可視為三部分:(1)圖形資料,(2)座標資料,(3)空間關係。在此篇論文中,我們僅考慮圖形資料中的點資料。在空間資料庫中,“尋找最近鄰居”的查詢即是目前最重要且熱門的空間查詢,例如:“開車路途中,離自己最近的加油站在哪裡?”而且在最近幾年,許多的研究都在於為“找尋最近鄰居的問題”謀得一個有效率的方法,以節省空間查詢的時間。找尋最近鄰居的問題即是在龐大的資料集中,搜尋離查詢點最近距離的一個點,其相關的應用在於網際地理資訊系統 (GIS) ,例如:電子地圖的應用。在一個二維平面上,區域 B 可以說是區域 A 的鄰居的條件是鄰近 A 且與 A 有相同的性質,例如相同的大小。Jozef Voros 在其研究中提出,在以 Quadtree 結構表示的圖形資料中,尋找某一查詢點位於區域 A 的四個相同大小鄰居區域(包括東﹑西﹑南﹑北的方向)的方法。然而以Voros 的方法為基礎,在對角線上的鄰居區域(包括東南﹑東北﹑西南﹑西北的方向)卻被忽略了,如此一來,離查詢點最近的點的正確性就會有些出入,因為最近點可能出現在對角線上的鄰居區域內。另外,當我們想將高維的空間資料轉換成一維資料時,最主要的困難在於無法用一個規則去規劃空間資料彼此的順序及距離。為了處理維度轉換的順序問題,相關的研究即是 Space-Filling Curves,這些曲線經過在空間中的每一筆資料,並以一對一的對應關係將高維的資料轉換成一維的號碼順序。Orenstein 提出的 Peano曲線,將二維的空間資料以穿插X與Y座標相對應二進位位元的方式轉換成一維的點資料。我們發現這個穿插位元的性質可以簡單地應用在找尋最近鄰居的問題上,因為可以由轉換後的一維資料去分析資料在空間中的位置。然而如果資料是以 RBG 曲線或 Hilbert 曲線串聯起來的,則因為曲線有經過旋轉或反射的特性,使得在這些曲線找尋鄰居的過程中,變得很複雜。RBG 曲線在範圍查詢中,能減少隨機存取次數。而 Hilbert 曲線在範圍查詢中,能達到存取的叢集數 (clusters) 最少。所以在此篇論文研究中,我們會先舉出 Voros的方法中忽略的情況,並設計方法來解決。接著,我們會提出如何以Peano曲線的方法來協助尋找最近鄰居的演算法。當資料以其他曲線表示時,我們歸納出在Peano 曲線及 RBG 曲線,在Peano 曲線及 Hilbert 曲線之間轉換的規則,以協助我們正確且快速地找到離查詢點最近的點資料。最後我們改進NA-Trees,它是一個龐大且動態的索引,可以處理直接對應查詢 (exact match query) 及範圍查詢 (range query)。所謂龐大,指的是絕大部分的索引資料都存在輔助記憶體中。所謂動態,指的是索引會隨著查詢而對樹結構作新增或刪除的動作,即索引不是事先建立好的。直接對應查詢是在空間資料庫中搜尋一筆符合查詢條件的資料,而範圍查詢則是在一個特定的範圍內,搜尋所有符合查詢條件的資料。
With the proliferation of wireless communications and rapid advances in technologies, algorithms for efficiently answering queries about large number of spatial data are needed. Spatial data consists of spatial objects including data of higher dimension. Neighbor finding is one of the most important spatial operations in the field of spatial data structures. In recent years, many
researchers have focused on finding efficient solutions to the nearest neighbor problem (NN) which involves determining the point in a data set that is the nearest to a given query point. It
is frequently used in Geographical Information Systems (GIS). A block B is said to be the neighbor of another block A, if block B has the same property as block A has and covers an
equal-sized neighbor of block A. Jozef Voros has proposed a neighbor finding strategy on images represented by quadtrees, in which the four equal-sized neighbors (the east, west, north, and south directions) of block A can be found. However, based on Voros''s strategy, the case that the nearest neighbor occurs in the diagonal directions (the northeast, northwest, southeast, and southwest directions) will be ignored. Moreover, there is no total ordering that preserve proximity when mapping a spatial data from a higher dimensional space to a 1D-space. One way of effecting such a mapping is to utilize
space-filling curves. Space-filling curves pass through every point in a space and give a one-one correspondence between the coordinate and the 1D-sequence number of the point. The Peano curve, proposed by Orenstein, which maps the 1D-coordinate of a point by simply interleaving the bits of the X and Y coordinates in the 2D-space, can be easily used in neighbor finding. But with the data ordered by the RBG curve or the Hilbert curve, the neighbor finding would be complex.
The RBG curve achieves savings in random accesses on the disk for range queries and the Hilbert curve achieves the best clustering for range queries. Therefore, in this thesis, we first show the missing case in the Voros''s strategy and show the ways to find it. Next, we show that the Peano curve is the best mapping function used in the nearest neighbor finding. We also show the
transformation rules between the Peano curve and the other curves such that we can efficiently find the nearest neighbor, when the data is linearly ordered by the other curves. From our simulation, we show that our proposed two strategies can work correctly and faster than the conventional strategies in nearest neighbor finding. Finally, we present a revised version of NA-Trees, which can work for exact match queries and range queries from a large, dynamic index, where an exact match query means finding the specific data object in a spatial database and a range query means reporting all data objects which are located in a specific range. By large, we mean that most of the index must be stored in secondary memory. By dynamic, we mean that insertions and deletions are intermixed with queries, so that the index cannot be built beforehand.
ABSTRACT
LIST OF FIGURES
LIST OF TABLES
1. Introduction
1.1 Spatial Databases
1.2 Query Types
1.3 The Nearest Neighbor
1.4 Motivations
2. Survey
2.1 Based on Quadtrees
2.1.1 Definition
2.1.2 Equal Sized Neighbors
2.2 Based on Space Filling Curves
2.2.1 Properties
2.2.2 Measures
2.3 Based on R-trees
3. A Note on Nearest Neighbor Finding in Images Represented by Quadtrees
3.1 A Missing Case
3.2 The Improved Version of Neighbor Finding
3.3 Examples
4. Neighbor Finding Based on Space Filling curves
4.1 Space Filling Curves
4.1.1 Region Code vs. Bit Shuffling
4.2 Nearest Neighbor Finding Based on the Peano Curve
4.3 Transformation Rules
4.3.1 Transformation Between the Peano Curve and the RBG Curve
4.3.2 Transformation Between the Peano Curve and the Hilbert Curve
5. The Revised Version of the NA-tree
5.1 Problems of NA-Trees
5.2 Data Structure
5.3 The Insertion Algorithm
5.4 The Deletion Algorithm
5.5 Exact Match and Range Queries
6. Performance Study
6.1 Simulation Study of the Nearest Neighbor Finding Strategies
6.2 Simulation Study of the NA-Trees
7. Conclusion
7.1 Summary
7.2 Future Work
BIBLIOGRAPHY
A. The Old Version of NA-Trees
A.1 The Bucket Numbering Schemes
A.2 Data Structure
A.3 Algorithms
B. Source code of for the Space-Filling Curves
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