天際線搜尋演算法在近期的資料庫研究領域中越趨重要。給定一組在多維度資料庫中的資料集，天際線搜尋會回傳那些不被其他點支配的資料點。在實務上，需要採用天際線搜尋的資料庫，通常都會提供多組候選維度，然而使用者只會對少部分有興趣。因此，通常都會根據多種維度的子集合進行查詢，而這類型的查詢稱之為子維度天際線查詢。使用傳統的天際線演算法來直接處理這種查詢是非常沒有效率的。有許多額外的演算法與架構被用來改善搜尋效率；然而，這些修改都會增加計算成本或是必須增加資料儲存空間。本篇論文提出一個基於Gaussian Function的全新索引模型，用來增強子維度天際線查詢的效率。實驗模擬結果展現了新的索引樹在子維度中找出天際線點的高效性。

The skyline search algorithm has recently emerged as an important technique in database research. Given a set of data points in a multi-dimensional database, such queries return points that are not “dominated” by any other point. In practice, databases that require a skyline query usually provide numerous candidate dimensions, of which users are interested in only a few. As a result, queries are issued regarding various sub-sets of the dimensions and such queries are called subspace skyline queries. Using the con-ventional skyline algorithm to process these queries directly can be extremely ineffective. Additional algorithms and architectures have been added to improve search efficiency; how-ever, such modifications can increase computa-tional costs or necessitate an increase in data storage capacity. This paper proposes a novel index model based on a Gaussian function to enhance the performance of subspace skyline queries. Simulation results demonstrate the effi-cacy of the proposed tree in locating skyline points within a subspace.