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have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date.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.

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Ranking techniques are effective at finding answers in document collections but can be expensive to evaluate. We propose an evaluation technique that uses early recognition of which documents are likely to be highly ranked to reduce costs; for our test data, queries are evaluated in 2% of the memory of the standard implementation without degradation in retrieval effectiveness. cpu time and disk traffic can also be dramatically reduced by designing inverted indexes explicitly to support the technique. The principle of the index design is that inverted lists are sorted by decreasing within-document frequency rather than by document number, and this method experimentally reduces cpu time and disk traffic to around one third of the original requirement. We also show that frequency sorting can lead to a net reduction in index size, regardless of whether the index is compressed.

this paper appears in [15], and updated information is available at http://cis.poly.edu/westlab/odissea/

With the proliferation of the world's "information highways" a renewed interest in efficient document indexing techniques has come about. In this paper, the problem of incremental updates of inverted lists is addressed using a new dual-structure index data structure. The index dynamically separates long and short inverted lists and optimizes the retrieval, update, and storage of each type of list. To study the behavior of the index, a space of engineering tradeoffs which range from optimizing update time to optimizing query performance is described. We quantitatively explore this space by using actual data and hardware in combination with a simulation of an information retrieval system. We then describe the best algorithm for a variety of criteria. 1 Introduction As the world's "information highways" proliferate and grow in capacity, they are providing access to an ever growing number of electronic document repositories. At each repository, the number of documents available on-line is...

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.

We introduce static index pruning methods that significantly reduce the index size in information retrieval systems. We investigate uniform and term-based methods that each remove selected entries from the index and yet have only a minor effect on retrieval results. In uniform pruning, there is a fixed cutoff threshold, and all index entries whose contribution to relevance scores is bounded above by a given threshold are removed from the index. In term-based pruning, the cutoff threshold is determined for each term, and thus may vary from term to term. We give experimental evidence that for each level of compression, term-based pruning outperforms uniform pruning, under various measures of precision. We present theoretical and experimental evidence that under our term-based pruning scheme, it is possible to prune the index greatly and still get retrieval results that are almost as good as those based on the full index. Topic areas: indexing, compression 1.

The performance of distributed text document retrieval systems is strongly in uenced bytheorganization of the inverted index. This paper compares the performance impact on query processing of various physical organizations for inverted lists. We present a new probabilistic model of the database and queries. Simulation experiments determine which variables most strongly inuence response time and throughput. This leadstoa set of design trade-o s over a range of hardware con gurations and new parallel query processing strategies. 1

Large web search engines have to answer thousands of queries per second with interactive response times. A major factor in the cost of executing a query is given by the lengths of the inverted lists for the query terms, which increase with the size of the document collection and are often in the range of many megabytes. To address this issue, IR and database researchers have proposed pruning techniques that compute or approximate term-based ranking functions without scanning over the full inverted lists.