目前，隨著資料急速地增加，大規模可擴充性的高度分散式資料庫服務已逐漸成為一種趨勢。在資料如此分散的環境下，如何讓資料的查詢更有效率，建立一個好的索引扮演著相當重要的角色，加上越來越多的資料庫程式應用像是生物、圖像、音樂和視訊等等，皆是處理高維度的資料，而在這些應用程式中，經常需要做相似資料的查詢，但是在高維度的資料且分散式的資料做相似資料的查詢，需耗費大量的時間與運算成本。 基於在高度分散式的環境下，針對高維度的資料有效地做KNN的查詢。我們提出一個利用reference point[2,13]的作法RP-CAN( Reference Point-Content Addressable Network )來改善查詢的效率。RP-CAN 主要是結合CAN [14] 的路由協定和使用reference point建立索引的方式來幫助在高度分散式環境下有效率的對高維的資料做查詢處理。 最後會實作出我們所提出的RP-CAN索引並與RT-CAN[1]做比較。我們發現我們所提出的RP-CAN索引在高維度資料作KNN的查詢時比RT-CAN索引來的有效率。 / There has been an increasing interest in deploying a storage system in a highly distributed environment because of the rapid increasing data. And many database applications such as time series, biological and multimedia database, handle high-dimensional data. In these systems, k nearest-neighbors query is one of the most frequent queries but costly operation that is to find objects in the high-dimensional database that are similar to a given query object. As in conventional DBMS, indexes can indeed improve query performance but cannot deploy directly in highly distributed systems because the environment has become more complex. To efficiently support k nearest-neighbors query, a high-dimensional indexing strategy, is developed for the highly distributed environment. In this paper, we propose an efficient indexing strategy, RP-CAN( Reference Point-Content Addressable Network ), to improve the performance of the k nearest-neighbors query in a highly distributed environment. In the end of this paper, we designed an experiment to demonstrate that the performance of RP-CAN is better than RT-CAN in high dimensional space. Thus, our RP-CAN index could efficiently handle the high dimensional data.