Finding relevant objects in a large repository is a fundamental research problem occurring in many applications, such as: data cleaning, data integration, web search, and information retrieval. Instant type-ahead fuzzy search, where user types her query character by character and find the top-k relevant objects, has become widely involved in many applications because it provides the users with rapid response results and improves the user’s experience. The state-of-the-art algorithms are generally inefficient due to their breadth first search algorithm that results in repeated computations.

To this end, we propose a novel depth-oriented instant type-ahead fuzzy search algorithm, that largely avoids repeated computations. The efficiency and effectiveness of the proposed approach are empirically demonstrated using real-world datasets. Experimental results show that our approach is 5–10 times faster than state-of-the-art approaches.

在大型存储库中查找相关对象是许多应用程序中出现的基本研究问题，例如：数据清理，数据集成，Web搜索和信息检索。即时提前输入模糊搜索（用户逐个字符地键入查询字符并找到与前k个相关的对象）已经广泛应用于许多应用程序中，因为它可以为用户提供快速的响应结果并改善用户的体验。由于其广度优先搜索算法会导致重复计算，因此现有技术通常效率低下。

为此，我们提出了一种新颖的面向深度的即时超前模糊搜索算法，该算法很大程度上避免了重复计算。使用现实世界的数据集，经验地证明了所提出方法的效率和有效性。实验结果表明，我们的方法比最先进的方法快5-10倍。