On the Precision of Search Engines: Results from a Controlled Experiment

Handling the growing amount of digital information is one of the major challenges when dealing with the World Wide Web (WWW). In particular, users crave for an effective and efficient retrieval of needed information. In this context, search engines adopt a key role. Besides conventional search engines such as Google, semantic search engines have emerged as an alternative approach in recent years. The quality of search results delivered by search engines is in influenced by many criteria. This paper picks up one specific issue, the precision, and investigates and compares the precision of current both conventional (i.e., non-semantic) and semantic search engines based on a controlled experiment with 77 participants. Specifically, Google, AltaVista, MetaGer, Hakia, Kngine, and WolframAlpha are investigated and compared

Load-Sensitive Selective Pruning for Distributed Search

A search engine infrastructure must be able to provide the same quality of service to all queries received during a day. During normal operating conditions, the demand for resources is considerably lower than under peak conditions, yet an oversized infrastructure would result in an unnecessary waste of computing power. A possible solution adopted in this situation might consist of defining a maximum threshold processing time for each query, and dropping queries for which this threshold elapses, leading to disappointed users. In this paper, we propose and evaluate a different approach, where, given a set of different query processing strategies with differing efficiency, each query is considered by a framework that sets a maximum query processing time and selects which processing strategy is the best for that query, such that the processing time for all queries is kept below the threshold. The processing time estimates used by the scheduler are learned from past queries. We experimentally validate our approach on 10,000 queries from a standard TREC dataset with over 50 million documents, and we compare it with several baselines. These experiments encompass testing the system under different query loads and different maximum tolerated query response times. Our results show that, at the cost of a marginal loss in terms of response quality, our search system is able to answer 90 % of queries within half a second during times of high query volume

Second chance: A hybrid approach for dynamic result caching and prefetching in search engines

Cataloged from PDF version of article.Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some data that is used to construct the final search result page returned to the user. An alternative strategy is to store in the cache only the result document IDs, which take much less space, allowing results of more queries to be cached. These two strategies lead to an interesting trade-off between the hit rate and the average query response latency. In this work, in order to exploit this trade-off, we propose a hybrid result caching strategy where a dynamic result cache is split into two sections: an HTML cache and a docID cache. Moreover, using a realistic cost model, we evaluate the performance of different result prefetching strategies for the proposed hybrid cache and the baseline HTML-only cache. Finally, we propose a machine learning approach to predict singleton queries, which occur only once in the query stream. We show that when the proposed hybrid result caching strategy is coupled with the singleton query predictor, the hit rate is further improved. © 2013 ACM

Soft-404 Pages, A Crawling Problem

[Absctract]: During its traversal of the Web, crawler systems have to deal with multiple challenges. Some of them are related with detecting garbage content to avoid wasting resources processing it. Soft-404 pages are a type of garbage content generated when some web servers do not use the appropriate HTTP response code for death links making them to be incorrectly identified. Our analysis of the Web has revealed that 7.35% of web servers send a 200 HTTP code when a request for an unknown document is received, instead of a 404 code, which indicates that the document is not found. This paper presents a system called Soft404Detector, based on web content analysis to identify web pages that are Soft-404 pages. Our system uses a set of content-based heuristics and combines them with a C4.5 classifier. For testing purposes, we built a Soft-404 pages dataset. Our experiments indicate that our system is very effective, achieving a precision of 0.992 and a recall of 0.980 at Soft-404 pages.This research was supported by Xunta de Galicia CN2012/211, the Ministry of Education and Science of Spain and FEDER funds of the European Union (Project TIN2009-14203).Xunta de Galicia; CN2012/21

SparkIR: a Scalable Distributed Information Retrieval Engine over Spark

Search engines have to deal with a huge amount of data (e.g., billions of documents in the case of the Web) and find scalable and efficient ways to produce effective search results. In this thesis, we propose to use Spark framework, an in memory distributed big data processing framework, and leverage its powerful capabilities of handling large amount of data to build an efficient and scalable experimental search engine over textual documents. The proposed system, SparkIR, can serve as a research framework for conducting information retrieval (IR) experiments. SparkIR supports two indexing schemes, document-based partitioning and term-based partitioning, to adopt document-at-a-time (DAAT) and term-at-a-time (TAAT) query evaluation methods. Moreover, it offers static and dynamic pruning to improve the retrieval efficiency. For static pruning, it employs champion list and tiering, while for dynamic pruning, it uses MaxScore top k retrieval. We evaluated the performance of SparkIR using ClueWeb12-B13 collection that contains about 50M English Web pages. Experiments over different subsets of the collection and compared the Elasticsearch baseline show that SparkIR exhibits reasonable efficiency and scalability performance overall for both indexing and retrieval. Implemented as an open-source library over Spark, users of SparkIR can also benefit from other Spark libraries (e.g., MLlib and GraphX), which, therefore, eliminates the need of usin

A machine learning approach for result caching in web search engines

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement. © 2017 Elsevier Lt

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

Web-scale profiling of semantic annotations in HTML pages

The vision of the Semantic Web was coined by Tim Berners-Lee almost two decades ago. The idea describes an extension of the existing Web in which “information is given well-defined meaning, better enabling computers and people to work in cooperation” [Berners-Lee et al., 2001]. Semantic annotations in HTML pages are one realization of this vision which was adopted by large numbers of web sites in the last years. Semantic annotations are integrated into the code of HTML pages using one of the three markup languages Microformats, RDFa, or Microdata. Major consumers of semantic annotations are the search engine companies Bing, Google, Yahoo!, and Yandex. They use semantic annotations from crawled web pages to enrich the presentation of search results and to complement their knowledge bases. However, outside the large search engine companies, little is known about the deployment of semantic annotations: How many web sites deploy semantic annotations? What are the topics covered by semantic annotations? How detailed are the annotations? Do web sites use semantic annotations correctly? Are semantic annotations useful for others than the search engine companies? And how can semantic annotations be gathered from the Web in that case? The thesis answers these questions by profiling the web-wide deployment of semantic annotations. The topic is approached in three consecutive steps: In the first step, two approaches for extracting semantic annotations from the Web are discussed. The thesis evaluates first the technique of focused crawling for harvesting semantic annotations. Afterward, a framework to extract semantic annotations from existing web crawl corpora is described. The two extraction approaches are then compared for the purpose of analyzing the deployment of semantic annotations in the Web. In the second step, the thesis analyzes the overall and markup language-specific adoption of semantic annotations. This empirical investigation is based on the largest web corpus that is available to the public. Further, the topics covered by deployed semantic annotations and their evolution over time are analyzed. Subsequent studies examine common errors within semantic annotations. In addition, the thesis analyzes the data overlap of the entities that are described by semantic annotations from the same and across different web sites. The third step narrows the focus of the analysis towards use case-specific issues. Based on the requirements of a marketplace, a news aggregator, and a travel portal the thesis empirically examines the utility of semantic annotations for these use cases. Additional experiments analyze the capability of product-related semantic annotations to be integrated into an existing product categorization schema. Especially, the potential of exploiting the diverse category information given by the web sites providing semantic annotations is evaluated