We offer an overview of current Web search engine design. After introducing a generic search engine architecture, we examine each engine component in turn. We cover crawling, local Web page storage, indexing, and the use of link analysis for boosting search performance. The most common design and implementation techniques for each of these components are presented. For this presentation we draw from the literature and from our own experimental search engine testbed. Emphasis is on introducing the fundamental concepts and the results of several performance analyses we conducted to compare different designs.

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 identify crucial design issues in building a distributed inverted index for a large collection of Web pages. We introduce a novel pipelining technique for structuring the core index-building system that substantially reduces the index construction time. We also propose a storage scheme for creating and managing inverted files using an embedded database system. We suggest and compare different strategies for collecting global statistics from distributed inverted indexes. Finally, we present performance results from experiments on a testbed distributed Web indexing system that we have implemented.

. Inverted index compression, block addressing and sequential search on compressed text are three techniques that have been separately developed for efficient, low-overhead text retrieval. Modern text compression techniques can reduce the text to less than 30% of its size and allow searching it directly and faster than the uncompressed text. Inverted index compression obtains significant reduction of their original size at the same processing speed. Block addressing makes the inverted lists point to text blocks instead of exact positions and pay the reduction in space with some sequential text scanning. In this work we combine the three ideas in a single scheme. We present a compressed inverted file that indexes compressed text and uses block addressing. We consider different techniques to compress the index and study their performance with respect to the block size. We compare the index against three separate techniques for varying block sizes, showing that our index is superior to each isolated approach. For instance, with just 4% of extra space overhead the index has to scan less than 12% of the text for exact searches and about 20% allowing one error in the matches. Keywords: Text compression, inverted files, block addressing, text databases. 1.

Efficient construction of inverted indexes is essential to provision of search over large collections of text data. In this paper, we review the principal approaches to inversion, analyse their theoretical cost, and present experimental results. We identify the drawbacks of existing inversion approaches and propose a single-pass inversion method that, in contrast to previous approaches, does not require the complete vocabulary of the indexed collection in main memory, can operate within limited resources, and does not sacrifice speed with high temporary storage requirements. We show that the performance of the single-pass approach can be improved by constructing inverted files in segments, reducing the cost of disk accesses during inversion of large volumes of data.

Indexes are the key technology underpinning efficient text search. A range of algorithms have been developed for fast query evaluation and for index creation, but update algorithms for high-performance indexes have not been evaluated or even fully described. In this paper, we explore the three main alternative strategies for index update: in-place update, index merging, and complete re-build. Our experiments with large volumes of web data show that re-merge is for large numbers of updates the fastest approach, but in-place update is suitable when the rate of update is low or buffer size is limited.

We present a technique to build an index based on suffix arrays for compressed texts. We also propose a compression scheme for textual databases based on words that generates a compression code that preserves the lexicographical ordering of the text words. As a consequence it permits the sorting of the compressed strings to generate the suffix array without decompressing. As the compressed text is under 30% of the size of the original text we are able to build the suffix array twice as fast on the compressed text. The compressed text plus index is 55-60% of the size of the original text plus index and search times are reduced to approximately half the time. We also present analytical and experimental results for different variations of the word-oriented compression paradigm.

Inverted index structures are the mainstay of modern text retrieval systems. They can be constructed quickly using off-line mergebased methods, and provide efficient support for a variety of querying modes. In this paper we examine the task of on-line index construction – that is, how to build an inverted index when the underlying data must be continuously queryable, and the documents must be indexed and available for search as soon they are inserted. When straightforward approaches are used, document insertions become increasingly expensive as the size of the database grows. This paper describes a mechanism based on controlled partitioning that can be adapted to suit different balances of insertion and querying operations, and is faster and scales better than previous methods. Using experiments on 100 GB of web data we demonstrate the efficiency of our methods in practice, showing that they dramatically reduce the cost of on-line index construction.

In designing data structures for text databases, it is valuable to know how many different words are likely to be encountered in a particular collection. For example, vocabulary accumulation is central to index construction for text database systems; it is useful to be able to estimate the space requirements and performance characteristics of the main-memory data structures used for this task. However, it is not clear how many distinct words will be found in a text collection or whether new words will continue to appear after inspecting large volumes of data. We propose practical definitions of a word, and investigate new word occurrences under these models in a large text collection. We inspected around two billion word occurrences in 45 gigabytes of world-wide web documents, and found just over 9.74 million different words in 5.5 million documents; overall, 1 word in 200 was new. We observe that new words continue to occur, even in very large data sets, and that choosing stricter definitions of what constitutes a word has only limited impact on the number of new words found.

We present a new family of hybrid index maintenance strategies to be used in on-line index construction for monotonically growing text collections. These new strategies improve upon recent results for hybrid index maintenance in dynamic text retrieval systems. Like previous techniques, our new method distinguishes between short and long posting lists: While short lists are maintained using a merge strategy, long lists are kept separate and are updated in-place. This way, costly relocations of long posting lists are avoided.