MWAND: A New Early Termination Algorithm for Fast and Efficient Query Evaluation

Nowadays, current information systems are so large and maintain huge amount of data. At every time, they process millions of documents and millions of queries. In order to choose the most important responses from this amount of data, it is well to apply what is so called early termination algorithms. These ones attempt to extract the Top-K documents according to a specified increasing monotone function. The principal idea behind is to reach and score the most significant less number of documents. So, they avoid fully processing the whole documents. WAND algorithm is at the state of the art in this area. Despite it is efficient, it is missing effectiveness and precision. In this paper, we propose two contributions, the principal proposal is a new early termination algorithm based on WAND approach, we call it MWAND (Modified WAND). This one is faster and more precise than the first. It has the ability to avoid unnecessary WAND steps. In this work, we integrate a tree structure as an index into WAND and we add new levels in query processing. In the second contribution, we define new fine metrics to ameliorate the evaluation of the retrieved information. The experimental results on real datasets show that MWAND is more efficient than the WAND approach

Static index pruning in web search engines: Combining term and document popularities with query views

Cataloged from PDF version of article.Static index pruning techniques permanently remove a presumably redundant part of an inverted file, to reduce the file size and query processing time. These techniques differ in deciding which parts of an index can be removed safely; that is, without changing the top-ranked query results. As defined in the literature, the query view of a document is the set of query terms that access to this particular document, that is, retrieves this document among its top results. In this paper, we first propose using query views to improve the quality of the top results compared against the original results. We incorporate query views in a number of static pruning strategies, namely term-centric, document-centric, term popularity based and document access popularity based approaches, and show that the new strategies considerably outperform their counterparts especially for the higher levels of pruning and for both disjunctive and conjunctive query processing. Additionally, we combine the notions of term and document access popularity to form new pruning strategies, and further extend these strategies with the query views. The new strategies improve the result quality especially for the conjunctive query processing, which is the default and most common search mode of a search engine

Static index pruning in web search engines

Static index pruning techniques permanently remove a presumably redundant part of an inverted file, to reduce the file size and query processing time. These techniques differ in deciding which parts of an index can be removed safely; that is, without changing the top-ranked query results. As defined in the literature, the query view of a document is the set of query terms that access to this particular document, that is, retrieves this document among its top results. In this paper, we first propose using query views to improve the quality of the top results compared against the original results. We incorporate query views in a number of static pruning strategies, namely term-centric, document-centric, term popularity based and document access popularity based approaches, and show that the new strategies considerably outperform their counterparts especially for the higher levels of pruning and for both disjunctive and conjunctive query processing. Additionally, we combine the notions of term and document access popularity to form new pruning strategies, and further extend these strategies with the query views. The new strategies improve the result quality especially for the conjunctive query processing, which is the default and most common search mode of a search engine

Static index pruning in web search engines: Combining term and document popularities with query views

Static index pruning techniques permanently remove a presumably redundant part of an inverted file, to reduce the file size and query processing time. These techniques differ in deciding which parts of an index can be removed safely; that is, without changing the top-ranked query results. As defined in the literature, the query view of a document is the set of query terms that access to this particular document, that is, retrieves this document among its top results. In this paper, we first propose using query views to improve the quality of the top results compared against the original results. We incorporate query views in a number of static pruning strategies, namely term-centric, document-centric, term popularity based and document access popularity based approaches, and show that the new strategies considerably outperform their counterparts especially for the higher levels of pruning and for both disjunctive and conjunctive query processing. Additionally,we combine the notions of term and document access popularity to form new pruning strategies, and further extend these strategies with the query views. The new strategies improve the result quality especially for the conjunctive query processing, which is the default and most common search mode of a search engine. © 2012 ACM

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

Pruning Statico di Posting a Basso Impatto su Indici per Sistemi di Information Retrieval

Il web mette a disposizione una vastissima collezione di documenti che i motori di ricerca devono accedere per fornire risultati alle richieste degli utenti. Al fine di rispondere in modo efficiente ad ogni richiesta, i motori di ricerca usano strutture dati chiamate indici, che sintetizzano l'informazione contenuta all'interno della collezione di documenti. La dimensione degli indici cresce al crescere del quantitativo di informazione da memorizzare e, con questa, cresce anche il tempo necessario per il recupero dell'informazione. L'obiettivo di questa tesi è quindi quello di proporre una strategia per l'eliminazione di informazione ritenuta non utile dall'indice, riducendone notevolmente la dimensione e quindi il tempo necessario per accedervi, preservando la qualità dei risultati dell'indice originale

Efficient Dynamic Pruning with Proximity Support

Modern retrieval approaches apply not just single-term weighting models when ranking documents- instead, proximity weighting models are in common use, which highly score the co-occurrence of pairs of query terms in close proximity to each other in documents. The adoption of these proximity weighting models can cause a computational overhead when documents are scored, negatively impacting the efficiency of the retrieval process. In this paper, we discuss the integration of proximity weighting models into efficient dynamic pruning strategies. In particular, we propose to modify document-at-a-time strategies to include proximity scoring without any modifications to pre-existing index structures. Our resulting two-stage dynamic pruning strategies only consider single query terms during first stage pruning, but can early terminate the proximity scoring of a document if it can be shown that it will never be retrieved. We empirically examine the efficiency benefits of our approach using a large Web test collection of 50 million documents and 10,000 queries from a real query log. Our results show that our proposed two-stage dynamic pruning strategies are considerably more efficient than the original strategies, particularly for queries of 3 or more terms