近幾年來，有很多研究將目標放在如何在伺服器端有效的管理這些物體，以提供使用者透過無線網路去對物體資料作查詢。
這樣的系統可以允許使用者在任何時間任何地點查詢他周圍附近物體的狀況，進而可以有進一步的反應動作。
這一類跟靜止或移動物體的所在位置相關之查詢被稱為基於位置之查詢(location-based queries)。
傳統的 location-based queries 大多是針對空間中單種類型的物體 (i.e., single data source) 作查詢，
像是找出離使用者最近的餐廳 (nearest neighbor queries) 或是找出在使用者週遭的電影院 (range queries) 。
然而在現實生活中，空間中存在著許多不同種類的物體，我們稱這些不同種類的物體為異種物體 (heterogeneous objects，簡稱為 HOs)。
如果在 HOs 中任兩個物體資料的距離都小於或等於 d，則此 HOs
被稱為異種鄰近資料 (heterogeneous neighboring objects, 簡稱為 HNOs)。
在本論文中，我們提出一種新穎且有用之 location-based query 來針對 HNOs 作處理，進而提供給使用者有用的 HNOs 資訊。
這個新穎的 location-based query 稱之為最短平均距離查詢 (shortest average-distance query，簡稱為 SADQ)。
考慮 n 類 HOs，分別為 HO1, HO2, ..., HOn。
假設在空間中有 m 組 HNOs，SADQ 會從這 m 組 HNOs 中找一個具有最短 average-distance 的 HNOs。
為了有效率的處理 shortest average-distance query，我們發展一個有效率的 SADQ 演算法。
此演算法可以降低在回答 shortest average-distance query 時的計算成本。我們實作大量的實驗來證明 SADQ 演算法之效益及效率。

In recent years, many researchers focus on how to effectively manage objects on the server side and then provide the user to query the objects over the wireless network.
Such a system allows user to query information of objects around him at anytime and anywhere.
This type of query associated with the location of static or moving objects is called the location-based query.
Most of processing techniques for the conventional location-based queries focus on single type of objects (i.e., single data source),
such as finding a nearest restaurant to the user (nearest neighbor queries) or finding the theaters closer to the user (range queries).
However, in real life, there are many different types of objects.
We term the different types of objects (i.e., multiple data source) the heterogeneous objects (HOs for short).
If the distance between any two objects in HOs is less than or equal to the user-defined distance d,
then the HOs is a heterogeneous neighboring objects (HNOs for short).
In this thesis, we propose a novel and useful location-based query on the HNOs, so as to provide the useful HNOs information.
Such a location-based query is named the shortest average-distance query (SADQ for short).
Consider the n types of data sources, HO1, HO2, ..., HOn.
Assume that there exist m sets of HNOs, SADQ finds a set of HNOs with the shortest average-distance among these m sets of HNOs.
To efficiently process the shortest average-distance query, we develop an efficient algorithm, the SADQ algorithm.
It can reduce the computation cost for answering the SADQ.
Comprehensive experiments are performed on both synthetic and real datasets to demonstrate the effectiveness and the
efficiency of the SADQ algorithm.

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