Efficient &amp; Effective Selective Query Rewriting with Efficiency Predictions

Macdonald, Craig; Tonellotto, Nicola; Ounis, Iadh

doi:10.1145/3077136.3080827

Cited by 12 publications

(12 citation statements)

References 34 publications

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“…We note that many more advanced prediction models have been explored in the literature, especially for quantifying the processing time of DAAT or TAAT traversal [119,129,162,166]. These models are almost certainly overly complicated for SAAT traversal prediction, as SAAT traversal is sensitive only to the number of postings to be processed [68].…”

Section: Threadsmentioning

confidence: 99%

Managing tail latency in large scale information retrieval systems

Mackenzie

2020

SIGIR Forum

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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 200 ms 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 query variations 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. 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.

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Section: Threadsmentioning

confidence: 99%

Managing tail latency in large scale information retrieval systems

Mackenzie

2020

SIGIR Forum

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“…Dierent query processing optimizations might be applied for dierent queries. For example, dierent query rewritings can be applied to the same query, with dierent cutos, depending on the specic terms appearing in the query [24]. Moreover, there are dierent optimizations of the search process and relative infrastructure that can be tuned, depending on the queries.…”

Section: Introductionmentioning

confidence: 99%

Using an Inverted Index Synopsis for Query Latency and Performance Prediction

Tonellotto

Macdonald

2020

ACM Trans. Inf. Syst.

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Predicting the query latency by a search engine has important benefits, for instance, in allowing the search engine to adjust its configuration to address long-running queries without unnecessarily sacrificing its effectiveness. However, for the dynamic pruning techniques that underlie many commercial search engines, achieving accurate predictions of query latencies is difficult. We propose the use of index synopses—which are stochastic samples of the full index—for attaining accurate timing predictions. Indeed, we experiment using the TREC ClueWeb09 collection, and a large set of real user queries, and find that using small index synopses it is possible to very accurately estimate properties of the larger index, including sizes of posting list unions and intersections. Thereafter, we demonstrate that index synopses facilitate two key use cases: first, for query efficiency prediction, we show that predicting the query latencies on the full index and classifying long-running queries can be accurately achieved using index synopses; second, for query performance prediction, we show that the effectiveness of queries can be estimated more accurately using a synopsis index post-retrieval predictor than a pre-retrieval predictor. Overall, our experiments demonstrate the value of such a stochastic sample of a larger index at predicting the properties of the larger index.

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“…Two noteworthy works that address QE efficiency are [11,18]. Macdonald et al [11] focus on the selection of different query rewritings.…”

Section: Introductionmentioning

confidence: 99%

“…Two noteworthy works that address QE efficiency are [11,18]. Macdonald et al [11] focus on the selection of different query rewritings. Specifically, they aim to predict the execution time of the query rewritings generated by different QE methods and then select the rewriting from the best-on-average source whose predicted cost is below a certain time budget.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Effective Query Expansion for Web Search

Lucchese

Nardini

Perego

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Query Expansion (QE) techniques expand the user queries with additional terms, e.g., synonyms and acronyms, to enhance the system recall. State-of-the-art solutions employ machine learning methods to select the most suitable terms. However, most of them neglect the cost of processing the expanded queries, thus selecting effective, yet very expensive, terms. The goal of this paper is to enable QE in scenarios with tight time constraints proposing a QE framework based on structured queries and efficiency-aware term selection strategies. In particular, the proposed expansion selection strategies aim at capturing the efficiency and the effectiveness of the expansion candidates, as well as the dependencies among them. We evaluate our proposals by conducting an extensive experimental assessment on real-world search engine data and public TREC data. Results confirm that our approach leads to a remarkable efficiency improvement w.r.t. the state-of-the-art: a reduction of the retrieval time up to 30 times, with only a small loss of effectiveness.

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Efficient & Effective Selective Query Rewriting with Efficiency Predictions

Cited by 12 publications

References 34 publications

Managing tail latency in large scale information retrieval systems

Managing tail latency in large scale information retrieval systems

Using an Inverted Index Synopsis for Query Latency and Performance Prediction

Efficient and Effective Query Expansion for Web Search

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