Abstract Indexing highly repetitive collections has become a relevant problem with the emergence of large repositories of versioned documents, among other applications. These collections may reach huge sizes, but are formed mostly of documents that are near-copies of others. Traditional techniques for indexing these collections fail to properly exploit their regularities in order to reduce space. We introduce new techniques for compressing inverted indexes that exploit this near-copy regularity. They are based on run-length, Lempel–Ziv, or grammar compression of the differential inverted lists, instead of the usual practice of gap-encoding them. We show that, in this highly repetitive setting, our compression methods significantly reduce the space obtained with classical techniques, at the price of moderate slowdowns. Moreover, our best methods are universal, that is, they do not need to know the versioning structure of the collection, nor that a clear versioning structure even exists. We also introduce compressed self-indexes in the comparison. These are designed for general strings (not only natural language texts) and represent the text collection plus the index structure (not an inverted index) in integrated form. We show that these techniques can compress much further, using a small fraction of the space required by our new inverted indexes. Yet, they are orders of magnitude slower.
The most relevant previous work targeting highly repetitive collections of natural language text is by @cite_18 @cite_34 . They presented alternative compression methods for non-positional indexes on versioned collections. Their approach, called two-level indexing , merges all the versions of each document for creating the inverted lists. A secondary index stores, for each entry of the main inverted list, a bitmap indicating the versions of the document that contain the term. They convert previous one-level'' techniques @cite_17 @cite_45 into two-level methods, and also study methods for reordering the versions in order to improve compression.
@cite_45 @cite_18 @cite_34 @cite_17
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Modern document collections often contain groups of documents with overlapping or shared content. However, most information retrieval systems process each document separately, causing shared content to be indexed multiple times. In this paper, we describe a new document representation model where related documents are organized as a tree, allowing shared content to be indexed just once. We show how this representation model can be encoded in an inverted index and we describe algorithms for evaluating free-text queries based on this encoding. We also show how our representation model applies to web, email, and newsgroup search. Finally, we present experimental results showing that our methods can provide a significant reduction in the size of an inverted index as well as in the time to build and query it. We study the problem of creating highly compressed full-text index structures for versioned document collections, that is, collections that contain multiple versions of each document. Important examples of such collections are Wikipedia or the web page archive maintained by the Internet Archive. A straightforward indexing approach would simply treat each document version as a separate document, such that index size scales linearly with the number of versions. However, several authors have recently studied approaches that exploit the significant similarities between different versions of the same document to obtain much smaller index sizes. In this paper, we propose new techniques for organizing and compressing inverted index structures for such collections. We also perform a detailed experimental comparison of new techniques and the existing techniques in the literature. Our results on an archive of the English version of Wikipedia, and on a subset of the Internet Archive collection, show significant benefits over previous approaches. Current Information Retrieval systems use inverted index structures for efficient query processing. Due to the extremely large size of many data sets, these index structures are usually kept in compressed form, and many techniques for optimizing compressed size and query processing speed have been proposed. In this paper, we focus on versioned document collections, that is, collections where each document is modified over time, resulting in multiple versions of the document. Consecutive versions of the same document are often similar, and several researchers have explored ideas for exploiting this similarity to decrease index size. We propose new index compression techniques for versioned document collections that achieve reductions in index size over previous methods. In particular, we first propose several bitwise compression techniques that achieve a compact index structure but that are too slow for most applications. Based on the lessons learned, we then propose additional techniques that come close to the sizes of the bitwise technique while also improving on the speed of the best previous methods. In this paper, we present an approach to the incorporation of object versioning into a distributed full-text information retrieval system. We propose an implementation based on “partially versioned” index sets, arguing that its space overhead and query-time performance make it suitable for full-text IR, with its heavy dependence on inverted indexing. We develop algorithms for computing both historical queries and time range queries and show how these algorithms can be applied to a number of problems in distributed information management, such as data replication, caching, transactional consistency, and hybrid media repositories.