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.
He and Suel @cite_51 also designed a positional inverted index for the repetitive scenario. They apply a previous technique to partition documents into fragments @cite_70 and then use their non-positional approach @cite_34 on the fragments. They focus on answering top-k queries, by first obtaining the top- @math ( @math ) documents over the non-positional index and then re-ranking them using the positional information in order to return the top- @math results. This is faster than using the whole positional information in the first stage @cite_51 @cite_44 .
@cite_70 @cite_44 @cite_34 @cite_51
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Current web search engines focus on searching only themost recentsnapshot of the web. In some cases, however, it would be desirableto search over collections that include many different crawls andversions of each page. One important example of such a collectionis the Internet Archive, though there are many others. Sincethe data size of such an archive is multiple times that of a singlesnapshot, this presents us with significant performance challenges.Current engines use various techniques for index compression andoptimized query execution, but these techniques do not exploit thesignificant similarities between different versions of a page, or betweendifferent pages.In this paper, we propose a general framework for indexing andquery processing of archival collections and, more generally, anycollections with a sufficient amount of redundancy. Our approachresults in significant reductions in index size and query processingcosts on such collections, and it is orthogonal to and can be combinedwith the existing techniques. It also supports highly efficientupdates, both locally and over a network. Within this framework,we describe and evaluate different implementations that trade offindex size versus CPU cost and other factors, and discuss applicationsranging from archival web search to local search of web sites,email archives, or file systems. We present experimental resultsbased on search engine query log and a large collection consistingof multiple crawls. The inverted file is the most popular indexing mechanism for document search in an information retrieval system. Compressing an inverted file can greatly improve document search rate. Traditionally, the d-gap technique is used in the inverted file compression by replacing document identifiers with usually much smaller gap values. However, fluctuating gap values cannot be efficiently compressed by some well-known prefix-free codes. To smoothen and reduce the gap values, we propose a document-identifier reassignment algorithm. This reassignment is based on a similarity factor between documents. We generate a reassignment order for all documents according to the similarity to reassign closer identifiers to the documents having closer relationships. Simulation results show that the average gap values of sample inverted files can be reduced by 30 , and the compression rate of d-gapped inverted file with prefix-free codes can be improved by 15 . 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. Versioned document collections are collections that contain multiple versions of each document. Important examples are Web archives, Wikipedia and other wikis, or source code and documents maintained in revision control systems. Versioned document collections can become very large, due to the need to retain past versions, but there is also a lot of redundancy between versions that can be exploited. Thus, versioned document collections are usually stored using special differential (delta) compression techniques, and a number of researchers have recently studied how to exploit this redundancy to obtain more succinct full-text index structures. In this paper, we study index organization and compression techniques for such versioned full-text index structures. In particular, we focus on the case of positional index structures, while most previous work has focused on the non-positional case. Building on earlier work in [zs:redun], we propose a framework for indexing and querying in versioned document collections that integrates non-positional and positional indexes to enable fast top-k query processing. Within this framework, we define and study the problem of minimizing positional index size through optimal substring partitioning. Experiments on Wikipedia and web archive data show that our techniques achieve significant reductions in index size over previous work while supporting very fast query processing.