考慮移動物體上與位置無關屬性之異質k近鄰監測

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研究生：	蘇郁琪 Yu-Chi Su
論文名稱：	考慮移動物體上與位置無關屬性之異質k近鄰監測 Monitoring Heterogeneous Nearest Neighbors for Moving Objects by Considering Location-independent Attributes
指導教授：	陳良弼 Arbee L.P. Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	46
中文關鍵詞：	異質k近鄰、移動物體
相關次數：	點閱：2 下載：0
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當今的空間查詢系統其空間資料(spatial data)含有幾個與位置無關之屬性。例如，以一個工作搜尋系統為例，此系統中的每一個工作(可視為一個物件) 皆含有與空間有關之屬性，例如：工作地點；亦含有與空間無關之屬性，例如：薪水。利用此工作搜尋系統，使用者可尋得一個離住所不遠、同時薪水又高的工作。因此，一個既可以同時考慮距離及薪水的查詢即可以滿足使用者的需求。在本篇論文中，為了區別傳統上只考慮與空間相關之屬性的k近鄰查詢，我們稱此查詢為「異質k近鄰查詢」。就我們目前所認知，此篇論文為第一篇於動態環境之下，針對異質k近鄰查詢所設計之全面解決方案。而先前針對傳統k近鄰的方法並不能延用於解決異質k近鄰問題。有鑑於此，本篇論文提出一個利用邊界性質來解決異質k近鄰問題的有效方法。我們更進一步提出了一個更新機制以動態監測連續性的異質k近鄰查詢。我們的實驗結果亦證明我們提出的方法十分有效率。

In some advanced applications, spatial data may have several location-independent attributes. For example, in a job finding database, each job opportunity (object) can be associated with both location-dependent attributes, e.g., the work location, and location-independent ones, e.g., the salary. A person who uses this database to find a job may prefer not only a shorter distance between his/her house and the work place but also a higher salary. Therefore, a query with both concepts of “shorter distance” and “higher salary” should be considered to meet the user’s needs. We call it the heterogeneous k-nearest neighbor (HkNN) query in distinction from the traditional k-nearest neighbor (kNN) query on spatial domain, which only concerns location-dependent attributes. To our knowledge, this thesis is the first work proposing a generic framework for solving the HkNN query in a dynamic environment in which the values of both the location-dependent attributes and the location-independent attributes of an object may change with time. Previous works on the traditional kNN problem cannot be applied to processing the HkNN query. In this thesis, we propose an efficient approach based on the bounding proprieties for the HkNN query evaluation. Furthermore, we provide an update mechanism for continuously monitoring the HkNN queries in a dynamic environment. Experimental results verify that the proposed framework is both efficient and scalable.

Contents
Abstract    i
Acknowledgements    ii
Contents    iii
List of Figures    iv
1.    Introduction    1
2.    Preliminaries    7
2.1    Related Work    7
2.2    Basic Definitions    9
3.    Efficient Evaluation of HKNN Queries    12
3.1    Data Structures    12
3.2    Problem Characteristic    14
3.3    Retrieving the First K Objects    15
3.4    HKNN Search with Pruning Mechanism    18
4.    Continuous Update of HKNN Query Answers    24
4.1    Process Single Object Update    24
4.2   Process Multiple Objects Updates    30
5.    Extension    33
6.    EXPERIMENTS    35
6.1    Experimental settings    35
6.2    Experimental results    36
7.    Conclusion and future work    43
Reference    44

List of Figures
Figure 1     An example of HkNN application    2
Figure 2     Hierarchical aggregate grid index    13
Figure 3     An example of HkNN query evaluation (a)    17
Figure 4     An example of HkNN query evaluation (b)    22
Figure 5     HkNN query evaluation algorithm    23
Figure 6     The distance and value influence region    25
Figure 7     Re-pruning algorithm    28
Figure 8     An example of distance and value influence region    30
Figure 9     HKNN query update algorithm    32
Figure 10   The road map of Oldenburg city    36
Figure 11   CPU Time vs. Grid granularity    38
Figure 12   CPU Time vs. Number of objects.    39
Figure 13   CPU Time vs. Number of queries    39
Figure 14   CPU Time vs. Number of k    40
Figure 15   CPU Time vs. Object mobility    42
Figure 16   CPU Time vs. Query mobility    42

                                

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