Artifact Representation Techniques for Large-Scale Software Search Engines

2013 ◽  
pp. 81-101 ◽  
Author(s):  
Oliver Hummel ◽  
Colin Atkinson ◽  
Marcus Schumacher
Keyword(s):  
Search Engines ◽  
Large Scale ◽  
Representation Techniques

scholarly journals The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Large Scale ◽  
Web Search ◽  
Primary Elections ◽  
Virtual Agents ◽  
Search Results ◽  
Presidential Primary ◽  
Large Scale Analysis ◽  
Algorithmic Information

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

Deep Web

2009 ◽  
pp. 581-588 ◽  
Author(s):  
Denis Shestakov
Keyword(s):  
Search Engines ◽  
Large Scale ◽  
Web Search ◽  
Web Database ◽  
Web Search Engine ◽  
Search Form ◽  
Complete Set ◽  
Web Crawlers ◽  
Pass Through ◽  
The Web

Finding information on the Web using a web search engine is one of the primary activities of today’s web users. For a majority of users results returned by conventional search engines are an essentially complete set of links to all pages on the Web relevant to their queries. However, currentday searchers do not crawl and index a significant portion of the Web and, hence, web users relying on search engines only are unable to discover and access a large amount of information from the nonindexable part of the Web. Specifically, dynamic pages generated based on parameters provided by a user via web search forms are not indexed by search engines and cannot be found in searchers’ results. Such search interfaces provide web users with an online access to myriads of databases on the Web. In order to obtain some information from a web database of interest, a user issues his/her query by specifying query terms in a search form and receives the query results, a set of dynamic pages which embed required information from a database. At the same time, issuing a query via an arbitrary search interface is an extremely complex task for any kind of automatic agents including web crawlers, which, at least up to the present day, do not even attempt to pass through web forms on a large scale.

Maximizing the sensitivity and reliability of peptide identification in large-scale proteomic experiments by harnessing multiple search engines

PROTEOMICS ◽  
2010 ◽  
Vol 10 (6) ◽  
pp. 1172-1189 ◽  
Author(s):  
Wen Yu ◽  
J. Alex Taylor ◽  
Michael T. Davis ◽  
Leo E. Bonilla ◽  
Kimberly A. Lee ◽  
...  
Keyword(s):  
Search Engines ◽  
Large Scale ◽  
Peptide Identification

Improving large-scale search engines with semantic annotations

2013 ◽  
Vol 40 (6) ◽  
pp. 2287-2296 ◽  
Author(s):  
Damaris Fuentes-Lorenzo ◽  
Norberto Fernández ◽  
Jesús A. Fisteus ◽  
Luis Sánchez
Keyword(s):  
Search Engines ◽  
Large Scale ◽  
Semantic Annotations

Resource-Efficient Index Shard Replication in Large Scale Search Engines

2019 ◽  
Vol 30 (12) ◽  
pp. 2820-2835
Author(s):  
Yusen Li ◽  
Xueyan Tang ◽  
Wentong Cai ◽  
Jiancong Tong ◽  
Xiaoguang Liu ◽  
...  
Keyword(s):  
Search Engines ◽  
Large Scale

Contextualized Clustering in Exploratory Web Search

2008 ◽  
pp. 184-207 ◽  
Author(s):  
Jon Atle Gulla ◽  
Hans Olaf Borch ◽  
Jon Espen Ingvaldsen
Keyword(s):  
Search Engines ◽  
Information Needs ◽  
Large Scale ◽  
Suffix Tree ◽  
Web Search ◽  
Amount Of Information ◽  
Acceptable Quality ◽  
Inherent Problem ◽  
Web Search Engines ◽  
The Web

Due to the large amount of information on the web and the difficulties of relating user’s expressed information needs to document content, large-scale web search engines tend to return thousands of ranked documents. This chapter discusses the use of clustering to help users navigate through the result sets and explore the domain. A newly developed system, HOBSearch, makes use of suffix tree clustering to overcome many of the weaknesses of traditional clustering approaches. Using result snippets rather than full documents, HOBSearch both speeds up clustering substantially and manages to tailor the clustering to the topics indicated in user’s query. An inherent problem with clustering, though, is the choice of cluster labels. Our experiments with HOBSearch show that cluster labels of an acceptable quality can be generated with no upervision or predefined structures and within the constraints given by large-scale web search.

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