The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.

Query processing costs on large text databases are dominated by the need to retrieve and scan the inverted list of each query term. Here we show that query response time for conjunctive Boolean queries and for informal ranked queries can be dramatically reduced, at little cost in terms of storage, by the inclusion of an internal index in each inverted list. This method has been applied in a retrieval system for a collection of nearly two million short documents. Our experimental results show that the selfindexing strategy adds less than 20% to the size of the inverted file, but, for Boolean queries of 5--10 terms, can reduce processing time to under one fifth of the previous cost. Similarly, ranked queries of 40--50 terms can be evaluated in as little as 25% of the previous time, with little or no loss of retrieval effectiveness.

Two well-known indexing methods are inverted files and signature files. We have undertaken a detailed comparison of these two approaches in the context of text indexing, paying particular attention to query evaluation speed and space requirements. We have examined their relative performance using both experimentation and a refined approach to modeling of signature files, and demonstrate that inverted files are distinctly superior to signature files. Not only can inverted files be used to evaluate typical queries in less time than can signature files, but inverted files require less space and provide greater functionality. Our results also show that a synthetic text database can provide a realistic indication of the behavior of an actual text database. The tools used to generate the synthetic database have been made publicly available.

... text. When searching complex or approximate patterns, our algorithms are up to 8 times faster than the search on uncompressed text. We also discuss the impact of our technique in inverted files pointing to logical blocks and argue for the possibility of keeping the text compressed all the time, decompressing only for displaying purposes.

. A new text compression scheme is presented in this paper. The main purpose of this scheme is to speed up string matching by searching the compressed file directly. The scheme requires no modification of the string-matching algorithm, which is used as a black box; any string-matching procedure can be used. Instead, the pattern is modified; only the outcome of the matching of the modified pattern against the compressed file is decompressed. Since the compressed file is smaller than the original file, the search is faster both in terms of I/O time and processing time than a search in the original file. For typical text files, we achieve about 30% reduction of space and slightly less of search time. A 30% space saving is not competitive with good text compression schemes, and thus should not be used where space is the predominant concern. The intended applications of this scheme are files that are searched often, such as catalogs, bibliographic files, and address books. Such files are ty...

this paper we show experimentally that, for large or small collections, storing integers in a compressed format reduces the time required for either sequential stream access or random access. We compare di#erent approaches to compressing integers, including the Elias gamma and delta codes, Golomb coding, and a variable-byte integer scheme. As a conclusion, we recommend that, for fast access to integers, files be stored compressed

Documents stored in a database system can have complex internal structure described by languages such as SGML. Howtotake advantage of this structure presents challenges for database system implementors. We classify the types of queries that need to be supported by SGML-conformant database systems. We then describe several data models that have been proposed for representing documents in a database system and discuss the support these models provide for SGML. Finally we consider query evaluation. 1

Approximate string matching is used for spelling correction and personal name matching. In this paper we show how to use string matching techniques in conjunction with lexicon indexes to find approximate matches in a large lexicon. We test several lexicon indexing techniques, including n-grams and permuted lexicons, and several string matching techniques, including string similarity measures and phonetic coding. We propose methods for combining these techniques, and show experimentally that these combinations yield good retrieval effectiveness while keeping index size and retrieval time low. Our experiments also suggest that, in contrast to previous claims, phonetic codings are markedly inferior to string distance measures, which are demonstrated to be suitable for both spelling correction and personal name matching. KEY WORDS: pattern matching; string indexing; approximate matching; compressed inverted files; Soundex

: Full-text retrieval systems typically use either a bitmap or an inverted file to identify which documents contain which words, so that the documents containing any combination of words can be quickly located. Bitmaps of word occurrences are large, but are usually sparse, and thus are amenable to a variety of compression techniques. Here we consider techniques in which the encoding of each bitvector within the bitmap is parameterised, so that a different code can be used for each bitvector. Our experimental results show that the new methods yield better compression than previous techniques. Categories and Subject Descriptors: E.4 [Coding and Information Theory]: Data compaction and compression; H.3.2 [Information Storage]: File organisation . Keywords: Full-text retrieval, data compression, document database, Huffman coding, geometric distribution, inverted file. 1 Introduction Full-text retrieval systems are used for storing and accessing document collections such as newspaper a...

We present a fast compression and decompression scheme for natural language texts that allows efficient and flexible string matching by searching the compressed text directly. The compression scheme uses a word-based Huffman encoding and the coding alphabet is byte-oriented rather than bit-oriented. We compress typical English texts to about 30% of their original size, against 40% and 35% for Compress and Gzip, respectively. Compression times are close to the times of Compress and approximately half the times of Gzip, and decompression times are lower than those of Gzip and one third of those of Compress. The searching algorithm allows a large number of variations of the exact and approximate compressed string matching problem, such as phrases, ranges, complements, wild cards and arbitrary regular expressions. Separators and stopwords can be discarded at search time without significantly increasing the cost. The algorithm is based on a word-oriented shift-or algorithm and a fast Boyer-Moore-type filter. It concomitantly uses the vocabulary of the text available as part of the Huffman coding data. When searching for simple patterns, our experiments show that running our algorithm on a compressed text is twice as fast as running Agrep on the uncompressed version of the same text. When searching complex or approximate patterns, our algorithm is up to 8 times faster than Agrep. We also mention the impact of our technique in inverted files pointing to documents or logical blocks as Glimpse.