The Potential of Learned Index Structures for Index Compression

Inverted indexes are vital in providing fast key-word-based search. For every term in the document collection, a list of identifiers of documents in which the term appears is stored, along with auxiliary information such as term frequency, and position offsets. While very effective, inverted indexes have large memory requirements for web-sized collections. Recently, the concept of learned index structures was introduced, where machine learned models replace common index structures such as B-tree-indexes, hash-indexes, and bloom-filters. These learned index structures require less memory, and can be computationally much faster than their traditional counterparts. In this paper, we consider whether such models may be applied to conjunctive Boolean querying. First, we investigate how a learned model can replace document postings of an inverted index, and then evaluate the compromises such an approach might have. Second, we evaluate the potential gains that can be achieved in terms of memory requirements. Our work shows that learned models have great potential in inverted indexing, and this direction seems to be a promising area for future research.Comment: Will appear in the proceedings of ADCS'1

A DFT-Based Running Time Prediction Algorithm for Web Queries

Web search engines are built from components capable of processing large amounts of user queries per second in a distributed way. Among them, the index service computes the topk documents that best match each incoming query by means of a document ranking operation. To achieve high performance, dynamic pruning techniques such as the WAND and BM-WAND algorithms are used to avoid fully processing all of the documents related to a query during the ranking operation. Additionally, the index service distributes the ranking operations among clusters of processors wherein in each processor multi-threading is applied to speed up query solution. In this scenario, a query running time prediction algorithm has practical applications in the efficient assignment of processors and threads to incoming queries. We propose a prediction algorithm for the WAND and BM-WAND algorithms. We experimentally show that our proposal is able to achieve accurate prediction results while significantly reducing execution time and memory consumption as compared against an alternative prediction algorithm. Our proposal applies the discrete Fourier transform (DFT) to represent key features affecting query running time whereas the resulting vectors are used to train a feed-forward neural network with back-propagation.Fil: Rojas, Oscar. Universidad de Santiago de Chile; ChileFil: Gil Costa, Graciela Verónica. Universidad Nacional de San Luis. Facultad de Ciencias Físico- Matemáticas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; ArgentinaFil: Marín, Mauricio. Universidad de Chile; Chil

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

Dynamic cutoff prediction in multi-stage retrieval systems

Modern multi-stage retrieval systems are comprised of a candidate generation stage followed by one or more reranking stages. In such an architecture, the quality of the final ranked list may not be sensitive to the quality of the initial candidate pool, especially in terms of early precision. This provides several opportunities to increase retrieval efficiency without significantly sacrificing effectiveness. In this paper, we explore a new approach to dynamically predicting the size of an initial result set in the candidate generation stage, which can directly affect the overall efficiency and effectiveness of the entire system. Previous work exploring this tradeoff has focused on global parameter settings that apply to all queries, even though optimal settings vary across queries. In contrast, we propose a technique that makes a parameter prediction to maximize efficiency within an effectiveness envelope on a per query basis, using only static pre-retrieval features. Experimental results show that substantial efficiency gains are achievable. In addition, our framework provides a versatile tool that can be used to estimate the effectiveness-efficiency tradeoffs that are possible before selecting and tuning algorithms to make machine-learned predictions