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

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

Temporal search in web archives

Web archives include both archives of contents originally published on the Web (e.g., the Internet Archive) but also archives of contents published long ago that are now accessible on the Web (e.g., the archive of The Times). Thanks to the increased awareness that web-born contents are worth preserving and to improved digitization techniques, web archives have grown in number and size. To unfold their full potential, search techniques are needed that consider their inherent special characteristics. This work addresses three important problems toward this objective and makes the following contributions: - We present the Time-Travel Inverted indeX (TTIX) as an efficient solution to time-travel text search in web archives, allowing users to search only the parts of the web archive that existed at a user's time of interest. - To counter negative effects that terminology evolution has on the quality of search results in web archives, we propose a novel query-reformulation technique, so that old but highly relevant documents are retrieved in response to today's queries. - For temporal information needs, for which the user is best satisfied by documents that refer to particular times, we describe a retrieval model that integrates temporal expressions (e.g., "in the 1990s") seamlessly into a language modelling approach. Experiments for each of the proposed methods show their efficiency and effectiveness, respectively, and demonstrate the viability of our approach to search in web archives.Webarchive bezeichnen einerseits Archive ursprünglich im Web veröffentlichter Inhalte (z. B. das Internet Archive), andererseits Archive, die vor langer Zeit veröffentlichter Inhalte im Web zugreifbar machen (z. B. das Archiv von The Times). Ein gewachsenes Bewusstein, dass originär digitale Inhalte bewahrenswert sind, sowie verbesserte Digitalisierungsverfahren haben dazu geführt, dass Anzahl und Umfang von Webarchiven zugenommen haben. Um das volle Potenzial von Webarchiven auszuschöpfen, bedarf es durchdachter Suchverfahren. Diese Arbeit befasst sich mit drei relevanten Teilproblemen und leistet die folgenden Beiträge: - Vorstellung des Time-Travel Inverted indeX (TTIX) als eine Erweiterung des invertierten Index, um Zeitreise-Textsuche auf Webarchiven effizient zu unterstützen. - Eine neue Methode zur automatischen Umformulierung von Suchanfragen, um negativen Auswirkungen entgegenzuwirken, die eine fortwährende Terminologieveränderung auf die Ergebnisgüte beim Suchen in Webarchiven hat. - Ein Retrieval-Modell, welches speziell auf Informationsbedürfnisse mit deutlichem Zeitbezug ausgerichtet ist. Dieses Retrieval-Modell bedient sich in Dokumenten enthaltener Zeitbezüge (z. B. "in the 1990s") und fügt diese nahtlos in einen auf Language Models beruhenden Retrieval-Ansatz ein. Zahlreiche Experimente zeigen die Effizienz bzw. Effektivität der genannten Beiträge und demonstrieren den praktischen Nutzen der vorgestellten Verfahren

Temporal search in web archives

Web archives include both archives of contents originally published on the Web (e.g., the Internet Archive) but also archives of contents published long ago that are now accessible on the Web (e.g., the archive of The Times). Thanks to the increased awareness that web-born contents are worth preserving and to improved digitization techniques, web archives have grown in number and size. To unfold their full potential, search techniques are needed that consider their inherent special characteristics. This work addresses three important problems toward this objective and makes the following contributions: - We present the Time-Travel Inverted indeX (TTIX) as an efficient solution to time-travel text search in web archives, allowing users to search only the parts of the web archive that existed at a user's time of interest. - To counter negative effects that terminology evolution has on the quality of search results in web archives, we propose a novel query-reformulation technique, so that old but highly relevant documents are retrieved in response to today's queries. - For temporal information needs, for which the user is best satisfied by documents that refer to particular times, we describe a retrieval model that integrates temporal expressions (e.g., "in the 1990s") seamlessly into a language modelling approach. Experiments for each of the proposed methods show their efficiency and effectiveness, respectively, and demonstrate the viability of our approach to search in web archives.Webarchive bezeichnen einerseits Archive ursprünglich im Web veröffentlichter Inhalte (z. B. das Internet Archive), andererseits Archive, die vor langer Zeit veröffentlichter Inhalte im Web zugreifbar machen (z. B. das Archiv von The Times). Ein gewachsenes Bewusstein, dass originär digitale Inhalte bewahrenswert sind, sowie verbesserte Digitalisierungsverfahren haben dazu geführt, dass Anzahl und Umfang von Webarchiven zugenommen haben. Um das volle Potenzial von Webarchiven auszuschöpfen, bedarf es durchdachter Suchverfahren. Diese Arbeit befasst sich mit drei relevanten Teilproblemen und leistet die folgenden Beiträge: - Vorstellung des Time-Travel Inverted indeX (TTIX) als eine Erweiterung des invertierten Index, um Zeitreise-Textsuche auf Webarchiven effizient zu unterstützen. - Eine neue Methode zur automatischen Umformulierung von Suchanfragen, um negativen Auswirkungen entgegenzuwirken, die eine fortwährende Terminologieveränderung auf die Ergebnisgüte beim Suchen in Webarchiven hat. - Ein Retrieval-Modell, welches speziell auf Informationsbedürfnisse mit deutlichem Zeitbezug ausgerichtet ist. Dieses Retrieval-Modell bedient sich in Dokumenten enthaltener Zeitbezüge (z. B. "in the 1990s") und fügt diese nahtlos in einen auf Language Models beruhenden Retrieval-Ansatz ein. Zahlreiche Experimente zeigen die Effizienz bzw. Effektivität der genannten Beiträge und demonstrieren den praktischen Nutzen der vorgestellten Verfahren

Temporal search in web archives

Web archives include both archives of contents originally published on the Web (e.g., the Internet Archive) but also archives of contents published long ago that are now accessible on the Web (e.g., the archive of The Times). Thanks to the increased awareness that web-born contents are worth preserving and to improved digitization techniques, web archives have grown in number and size. To unfold their full potential, search techniques are needed that consider their inherent special characteristics. This work addresses three important problems toward this objective and makes the following contributions: - We present the Time-Travel Inverted indeX (TTIX) as an efficient solution to time-travel text search in web archives, allowing users to search only the parts of the web archive that existed at a user's time of interest. - To counter negative effects that terminology evolution has on the quality of search results in web archives, we propose a novel query-reformulation technique, so that old but highly relevant documents are retrieved in response to today's queries. - For temporal information needs, for which the user is best satisfied by documents that refer to particular times, we describe a retrieval model that integrates temporal expressions (e.g., "in the 1990s") seamlessly into a language modelling approach. Experiments for each of the proposed methods show their efficiency and effectiveness, respectively, and demonstrate the viability of our approach to search in web archives.Webarchive bezeichnen einerseits Archive ursprünglich im Web veröffentlichter Inhalte (z. B. das Internet Archive), andererseits Archive, die vor langer Zeit veröffentlichter Inhalte im Web zugreifbar machen (z. B. das Archiv von The Times). Ein gewachsenes Bewusstein, dass originär digitale Inhalte bewahrenswert sind, sowie verbesserte Digitalisierungsverfahren haben dazu geführt, dass Anzahl und Umfang von Webarchiven zugenommen haben. Um das volle Potenzial von Webarchiven auszuschöpfen, bedarf es durchdachter Suchverfahren. Diese Arbeit befasst sich mit drei relevanten Teilproblemen und leistet die folgenden Beiträge: - Vorstellung des Time-Travel Inverted indeX (TTIX) als eine Erweiterung des invertierten Index, um Zeitreise-Textsuche auf Webarchiven effizient zu unterstützen. - Eine neue Methode zur automatischen Umformulierung von Suchanfragen, um negativen Auswirkungen entgegenzuwirken, die eine fortwährende Terminologieveränderung auf die Ergebnisgüte beim Suchen in Webarchiven hat. - Ein Retrieval-Modell, welches speziell auf Informationsbedürfnisse mit deutlichem Zeitbezug ausgerichtet ist. Dieses Retrieval-Modell bedient sich in Dokumenten enthaltener Zeitbezüge (z. B. "in the 1990s") und fügt diese nahtlos in einen auf Language Models beruhenden Retrieval-Ansatz ein. Zahlreiche Experimente zeigen die Effizienz bzw. Effektivität der genannten Beiträge und demonstrieren den praktischen Nutzen der vorgestellten Verfahren