Document Retrieval on Repetitive Collections

Document retrieval aims at finding the most important documents where a pattern appears in a collection of strings. Traditional pattern-matching techniques yield brute-force document retrieval solutions, which has motivated the research on tailored indexes that offer near-optimal performance. However, an experimental study establishing which alternatives are actually better than brute force, and which perform best depending on the collection characteristics, has not been carried out. In this paper we address this shortcoming by exploring the relationship between the nature of the underlying collection and the performance of current methods. Via extensive experiments we show that established solutions are often beaten in practice by brute-force alternatives. We also design new methods that offer superior time/space trade-offs, particularly on repetitive collections.Comment: Accepted to ESA 2014. Implementation and experiments at http://www.cs.helsinki.fi/group/suds/rlcsa

The Quantile Index - Succinct Self-Index for Top-k Document Retrieval

One of the central problems in information retrieval is that of finding the k documents in a large text collection that best match a query given by a user. A recent result of Navarro & Nekrich (SODA 2012) showed that single term and phrase queries of length m can be solved in optimal O(m+k) time using a linear word sized index. While a verbatim implementation of the index would be at least an order of magnitude larger than the original collection, various authors incrementally improved the index to a point where the space requirement is currently within a factor of 1.5 to 2.0 of the text size for standard collections. In this paper, we propose a new time/space trade-off for different top-k indexes. This is achieved by sampling only a quantile of the postings in the original inverted file or suffix array-based index. For those queries that cannot be answered using the sampled version of the index we show how to compute the query results on the fly efficiently. As an example, we apply our method to the top-k framework by Navarro & Nekrich. Under probabilistic assumptions that hold for most standard texts, and for a standard scoring function called term frequency, our index can be represented with only sublinearly many bits plus the space needed for a compressed suffix array of the text, while maintaining poly-logarithmic query times. We evaluate our solution on real-world datasets and compare its practical space usage and performance against state-of-the-art implementations. Our experiments show that our index compresses below the size of the original text. To our knowledge it is the first suffix array-based text index that is able to break this bound in practice even for non-repetitive collections, while still maintaining reasonable query times of under half a millisecond on average for top-10 queries

Document retrieval on repetitive string collections

Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists, that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top-k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.Peer reviewe

Space-efficient data structures for string searching and retrieval

Let D = {d_1, d_2, ...} be a collection of string documents of n characters in total, which are drawn from an alphabet set Sigma =[sigma] ={1,2,3,...sigma}. The top-k document retrieval problem is to maintain D as a data structure, such that when ever a query Q=(P, k) comes, we can report (the identifiers of) those k documents that are most relevant to the pattern P (of p characters). The relevance of a document d_r with respect to a pattern P is captured by score(P, d_r), which can be any function of the set of locations where P occurs in d_r. Finding the most relevant documents to the user query is the central task of any web-search engine. In the case of web-data, the documents can be demarcated along word boundaries. All the search engines use inverted index as the back-bone data structure. For each word occurring in the document collection, the inverted index stores the list of documents where it appears. It is often augmented with relevance score and/or positional information. However, when data consists of strings (e.g., in bioinformatics or Asian language texts), there are no word demarcation boundaries and the queries are arbitrary substrings instead of being proper valid words. In this case, string data structures have to be used and central approach is to use suffix tree (or string B-tree) with appropriate augmenting data structures. The work by Hon, Shah and Vitter [FOCS 2009], and Navarro and Nekrich [SODA 2012] resulted in a linear space data structure with optimal O(p+k) query time solution for this problem. This was based on geometric interpretation of the query. We extend this central problem, in two important areas of massive data sets. First, we consider an external memory disk based index, where we give near optimal results. Next, we consider compression aspects of data structure, reducing the storage space. This is central goal of the active research field of succinct data structures. We present several results, which improve upon several previous results, and are currently the best known space-time trade-offs in this area

Faster Compact Top-k Document Retrieval

An optimal index solving top-k document retrieval [Navarro and Nekrich, SODA’12] takes O(m + k) time for a pattern of length m, but its space is at least 80n bytes for a collection of n symbols. We reduce it to 1.5n– 3n bytes, with O(m+(k+log log n) log log n) time, on typical texts. The index is up to 25 times faster than the best previous compressed solutions, and requires at most 5 % more space in practice (and in some cases as little as one half). Apart from replacing classical by compressed data structures, our main idea is to replace suffix tree sampling by frequency thresholding to achieve compression

Space-Efficient Data Structures for Information Retrieval

The amount of data that people and companies store has grown exponentially over the last few years. Storing this information alone is not enough, because in order to make it useful we need to be able to efficiently search inside it. Furthermore, it is highly valuable to keep the historic data of each document stored, allowing to not only access and search inside the newest version, but also over the whole history of the documents. Grammar-based compression has proven to be very effective for repetitive data, which is the case for versioned documents. In this thesis we present several results on representing textual information and searching in it. In particular, we present text indexes for grammar-based compressed text that support searching for a pattern and extracting substrings of the input text. These are the first general indexes for grammar-based compressed text that support searching in sublinear time. In order to build our indexes, we present new results on representing binary relations in a space-efficient manner, and construction algorithms that use little space to achieve their goal. These two results have a wide range of applications. In particular, the representations for binary relations can be used as a building block for several structures in computer science, such as graphs, inverted indexes, etc. Finally, we present a new index, that uses on grammar-based compression, to solve the document listing problem. This problem deals with representing a collection of texts and searching for the documents that contain a given pattern. In spite of being similar to the classical text indexing problem, this problem has proven to be a challenge when we do not want to pay time proportional to the number of occurrences, but time proportional to the size of the result. Our proposal is designed particularly for versioned text, allowing the storage of a collection of documents with all their historic versions in little space. This is currently the smallest structure for such a purpose in practice