In the document retrieval problem, we are given a collection of documents (strings) of total length D in advance, and our target is to create an index for these documents such that for any subsequent input pattern P,
we can identify which documents in the collection contain P. In this thesis, we study a natural extension to the above document retrieval problem. We call this top-k frequent document retrieval, where instead of listing all documents containing P, our focus is to identify the top k documents having most occurrences of P. This problem forms a basis for search engine tasks of retrieving documents ranked with TFIDF metric.
A related problem was studied where the emphasis was on retrieving all the documents whose number of occurrences of the pattern P exceeds some frequency threshold f. However, from the information retrieval point of view, it is hard for a user to specify such a threshold value f and have a sense of how many documents will be outputted. We develop
some additional building blocks which help the user overcome this limitation. These are used to derive an efficient index for top-k frequent document retrieval problem, answering queries in O(|P| + logDloglogD + k) time and taking O(DlogD) space. Our approach is based on novel use of the suffix tree called induced generalized suffix tree (IGST).

在文件檢索的問題中，我們給定一個總長度為 D 的文件（長字串）集合，目標是要對此文件集合建立索引，使得對任意一個 P 字串，我們能夠快速地知道哪些文件有包含 P。在這篇論文，我們提出了以上問題的一個延伸，名為 top-k 文件檢索。在此問題，我們並不會列出所有包含 P 的文件，而是只把 P 出現次數最多的 k 個文件列出。這個問題是搜尋引擎的根基。

Muthukrishnan 曾提出一個相關問題。他是要找出哪些文章 P 出現的次數超過 f 次。然而從資訊檢索的觀點來看，使用者很難知道要令 f 為多少，才能得到一個合理的或有意義的文件輸出量。在此論文，我們針對以上情形提出一些解法，並藉此得到有效率的 top-k 文件檢索的索引。我們的索引能夠在 O(|P| + logDloglogD + k) 時間內檢索，且只花 O(DlogD) 空間。我們的方法是根據廣為使用的字尾樹 (Suffix Tree) 的變形，在此我們稱之為導出字尾樹 (Induced Generalized Suffix Tree)。

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