Index ordering by query-independent measures

Conventional approaches to information retrieval search through all applicable entries in an inverted file for a particular collection in order to find those documents with the highest scores. For particularly large collections this may be extremely time consuming. A solution to this problem is to only search a limited amount of the collection at query-time, in order to speed up the retrieval process. In doing this we can also limit the loss in retrieval efficacy (in terms of accuracy of results). The way we achieve this is to firstly identify the most “important” documents within the collection, and sort documents within inverted file lists in order of this “importance”. In this way we limit the amount of information to be searched at query time by eliminating documents of lesser importance, which not only makes the search more efficient, but also limits loss in retrieval accuracy. Our experiments, carried out on the TREC Terabyte collection, report significant savings, in terms of number of postings examined, without significant loss of effectiveness when based on several measures of importance used in isolation, and in combination. Our results point to several ways in which the computation cost of searching large collections of documents can be significantly reduced

Development and Performance Evaluation of a Real-Time Web Search Engine

As the World Wide Web continues to grow, the tools to retrieve the information must develop in terms of locating web pages, categorizing content, and retrieving quality pages. Web search engines have enhanced the online experience by making pages easier to find. Search engines have made a science of cataloging page content, but the data can age, becoming outdated and irrelevant. By searching pages in real time in a localized area of the web, information that is retrieved is guaranteed to be available at the time of the search. The real-time search engines intriguing premise provides an overwhelming challenge. Since the web is searched in real time, the engine\u27s execution will take longer than traditional search engines. The challenge is to determine what factors can enhance the performance of the real-time search engine. This research takes a look at three components: traversal methodologies for searching the web, utilizing concurrently executing spiders, and implementing a caching resource to reduce the execution time of the real-time search engine. These components represent some basic methodologies to improve performance. By determining which implementations provide the best response, a better and faster real-time search engine can become a useful searching tool for Internet users

Search engine optimisation using past queries

World Wide Web search engines process millions of queries per day from users all over the world. Efficient query evaluation is achieved through the use of an inverted index, where, for each word in the collection the index maintains a list of the documents in which the word occurs. Query processing may also require access to document specific statistics, such as document length; access to word statistics, such as the number of unique documents in which a word occurs; and collection specific statistics, such as the number of documents in the collection. The index maintains individual data structures for each these sources of information, and repeatedly accesses each to process a query. A by-product of a web search engine is a list of all queries entered into the engine: a query log. Analyses of query logs have shown repetition of query terms in the requests made to the search system. In this work we explore techniques that take advantage of the repetition of user queries to improve the accuracy or efficiency of text search. We introduce an index organisation scheme that favours those documents that are most frequently requested by users and show that, in combination with early termination heuristics, query processing time can be dramatically reduced without reducing the accuracy of the search results. We examine the stability of such an ordering and show that an index based on as little as 100,000 training queries can support at least 20 million requests. We show the correlation between frequently accessed documents and relevance, and attempt to exploit the demonstrated relationship to improve search effectiveness. Finally, we deconstruct the search process to show that query time redundancy can be exploited at various levels of the search process. We develop a model that illustrates the improvements that can be achieved in query processing time by caching different components of a search system. This model is then validated by simulation using a document collection and query log. Results on our test data show that a well-designed cache can reduce disk activity by more than 30%, with a cache that is one tenth the size of the collection

Efficient query processing for scalable web search

Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware and software architectures

Real-time Text Queries with Tunable Term Pair Indexes

Term proximity scoring is an established means in information retrieval for improving result quality of full-text queries. Integrating such proximity scores into efficient query processing, however, has not been equally well studied. Existing methods make use of precomputed lists of documents where tuples of terms, usually pairs, occur together, usually incurring a huge index size compared to term-only indexes. This paper introduces a joint framework for trading off index size and result quality, and provides optimization techniques for tuning precomputed indexes towards either maximal result quality or maximal query processing performance, given an upper bound for the index size. The framework allows to selectively materialize lists for pairs based on a query log to further reduce index size. Extensive experiments with two large text collections demonstrate runtime improvements of several orders of magnitude over existing text-based processing techniques with reasonable index sizes

Document replication strategies for geographically distributed web search engines

Cataloged from PDF version of article.Large-scale web search engines are composed of multiple data centers that are geographically distant to each other. Typically, a user query is processed in a data center that is geographically close to the origin of the query, over a replica of the entire web index. Compared to a centralized, single-center search engine, this architecture offers lower query response times as the network latencies between the users and data centers are reduced. However, it does not scale well with increasing index sizes and query traffic volumes because queries are evaluated on the entire web index, which has to be replicated and maintained in all data centers. As a remedy to this scalability problem, we propose a document replication framework in which documents are selectively replicated on data centers based on regional user interests. Within this framework, we propose three different document replication strategies, each optimizing a different objective: reducing the potential search quality loss, the average query response time, or the total query workload of the search system. For all three strategies, we consider two alternative types of capacity constraints on index sizes of data centers. Moreover, we investigate the performance impact of query forwarding and result caching. We evaluate our strategies via detailed simulations, using a large query log and a document collection obtained from the Yahoo! web search engine. (C) 2012 Elsevier Ltd. All rights reserved

Estrategias algorítmicas y estructuras de datos eficientes para búsquedas en datos masivos

El mundo digital nos expone diariamente a una cantidad de datos constantemente creciente que exige contar con herramientas eficaces y muy eficientes para procesarlos y accederlos. La diversidad de aplicaciones que producen y consumen datos, sumada a un número también creciente de usuarios impone desafíos computacionales, tanto algorítmicos como del hardware disponible. Ejemplos típicos son sistemas de búsquedas de gran escala (como los motores de búsqueda web) o los servicios de búsqueda en tiempo real (como aquellos disponibles en las redes sociales). Estos escenarios no solo exigen mayores capacidades a los proveedores de servicios (lo que impacta en su operación) sino, además, mejoras conceptuales y prácticas en las estructuras de datos y los algoritmos necesarios para que los sistemas escalen adecuadamente y puedan gestionar la demanda. La eficiencia es un requerimiento fundamental en el mundo digital actual caracterizado por datos masivos, heterogéneos y dinámicos. Estas líneas de investigación abordan problemas de búsqueda en datos masivos, tanto desde las estructuras de datos como de los algoritmos necesarios para procesar documentos, publicaciones en redes sociales o consultas, con el objetivo de posibilitar la escalabilidad de los sistemas de búsqueda con el objetivo final de hacer un uso más racional de los recursos.Red de Universidades con Carreras en Informátic

On inverted index compression for search engine efficiency

Efficient access to the inverted index data structure is a key aspect for a search engine to achieve fast response times to users’ queries . While the performance of an information retrieval (IR) system can be enhanced through the compression of its posting lists, there is little recent work in the literature that thoroughly compares and analyses the performance of modern integer compression schemes across different types of posting information (document ids, frequencies, positions). In this paper, we experiment with different modern integer compression algorithms, integrating these into a modern IR system. Through comprehensive experiments conducted on two large, widely used document corpora and large query sets, our results show the benefit of compression for different types of posting information to the space- and time-efficiency of the search engine. Overall, we find that the simple Frame of Reference compression scheme results in the best query response times for all types of posting information. Moreover, we observe that the frequency and position posting information in Web corpora that have large volumes of anchor text are more challenging to compress, yet compression is beneficial in reducing average query response times

Managing tail latency in large scale information retrieval systems

As both the availability of internet access and the prominence of smart devices continue to increase, data is being generated at a rate faster than ever before. This massive increase in data production comes with many challenges, including efficiency concerns for the storage and retrieval of such large-scale data. However, users have grown to expect the sub-second response times that are common in most modern search engines, creating a problem - how can such large amounts of data continue to be served efficiently enough to satisfy end users? This dissertation investigates several issues regarding tail latency in large-scale information retrieval systems. Tail latency corresponds to the high percentile latency that is observed from a system - in the case of search, this latency typically corresponds to how long it takes for a query to be processed. In particular, keeping tail latency as low as possible translates to a good experience for all users, as tail latency is directly related to the worst-case latency and hence, the worst possible user experience. The key idea in targeting tail latency is to move from questions such as "what is the median latency of our search engine?" to questions which more accurately capture user experience such as "how many queries take more than 200ms to return answers?" or "what is the worst case latency that a user may be subject to, and how often might it occur?" While various strategies exist for efficiently processing queries over large textual corpora, prior research has focused almost entirely on improvements to the average processing time or cost of search systems. As a first contribution, we examine some state-of-the-art retrieval algorithms for two popular index organizations, and discuss the trade-offs between them, paying special attention to the notion of tail latency. This research uncovers a number of observations that are subsequently leveraged for improved search efficiency and effectiveness. We then propose and solve a new problem, which involves processing a number of related queries together, known as multi-queries, to yield higher quality search results. We experiment with a number of algorithmic approaches to efficiently process these multi-queries, and report on the cost, efficiency, and effectiveness trade-offs present with each. Ultimately, we find that some solutions yield a low tail latency, and are hence suitable for use in real-time search environments. Finally, we examine how predictive models can be used to improve the tail latency and end-to-end cost of a commonly used multi-stage retrieval architecture without impacting result effectiveness. By combining ideas from numerous areas of information retrieval, we propose a prediction framework which can be used for training and evaluating several efficiency/effectiveness trade-off parameters, resulting in improved trade-offs between cost, result quality, and tail latency