Abstract
The growing sizes of text repositories on the world wide web has created a need for efficient indexing and retrieval methods for text collections. Almost all of the text retrieval and indexing methods have been designed for the case of simple keyword search, in which a few keywords are specified, and the text is retrieved on the basis of matches to these keywords. However, in many applications there is a need for a greater specificity during the search, such as the use of phrases, sentences, text fragments, or even documents for the retrieval process. An even more general case is one in which a collection of documents is available as a query to the search process. In such cases, it is desirable to return sets of all pairwise similar documents. Such queries are referred to as corpus to corpus queries, and are computationally intensive because of the very large number of document pairs which need to be compared. Such cases cannot be efficiently processed by the available indexing and searching methods. Most of the currently available techniques can index the text based on only a small number of keywords or representative phrases. In this paper, we design a compressed finger print index which can support the following more general queries: (a) The method can process very efficient document-to-corpus search because of their efficient bit-wise operations for the search process. (b) We further extend the method to work for corpus-to-corpus queries, in which it is desirable to determine the most similar pairs of documents in two collections. We design an efficient search technique which is able to reduce the search time for large collections. The key technique used to enable this is an efficient fingerprint representation, which can be used effectively for the search process. To the best of our knowledge, this is the first work on corpus-based search in massive document collections. © 2012 ACM.