scholarly journals Neural Networks in Big Data and Web Search

Data ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 7 ◽  
Author(s):  
Will Serrano
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Recommender Systems ◽  
User Experience ◽  
Search Engines ◽  
Web Search ◽  
Practical Applications ◽  
Survey Paper ◽  
Search Service ◽  
Web Search Engines

As digitalization is gradually transforming reality into Big Data, Web search engines and recommender systems are fundamental user experience interfaces to make the generated Big Data within the Web as visible or invisible information to Web users. In addition to the challenge of crawling and indexing information within the enormous size and scale of the Internet, e-commerce customers and general Web users should not stay confident that the products suggested or results displayed are either complete or relevant to their search aspirations due to the commercial background of the search service. The economic priority of Web-related businesses requires a higher rank on Web snippets or product suggestions in order to receive additional customers. On the other hand, web search engine and recommender system revenue is obtained from advertisements and pay-per-click. The essential user experience is the self-assurance that the results provided are relevant and exhaustive. This survey paper presents a review of neural networks in Big Data and web search that covers web search engines, ranking algorithms, citation analysis and recommender systems. The use of artificial intelligence (AI) based on neural networks and deep learning in learning relevance and ranking is also analyzed, including its utilization in Big Data analysis and semantic applications. Finally, the random neural network is presented with its practical applications to reasoning approaches for knowledge extraction.

A Semantic-Based Dynamic Search Engine Design and Implementation for Electronic Medical Records

2012 ◽  
pp. 285-297
Author(s):  
Weider D. Yu ◽  
Seshadri K. Yilayavilli
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
First Generation ◽  
Web Search ◽  
Relevant Information ◽  
Engine Design ◽  
Search Service ◽  
Web Search Engines ◽  
World Information ◽  
The Right

In the current technology driven world, information retrieval activities are in almost every aspect of daily, as society uses popular web search engines like Google, Yahoo!, Live Search, Ask, and so forth to obtain helpful information. Often, these popular search engines look for and obtain key information; however, not all of the retrieved items are relevant in context to the search target a. Thus, it is left for the user to filter out unwanted information, using only a few information items left from the search results. These popular web search engines use a first generation search service based on “static keywords”, which require the users to know exactly what they want to search and enter the right keywords. This approach puts the user at a disadvantage. In this paper, the authors investigate and design a dynamic, question-answer search engine that enables searching by attributes for more precise and relevant information in Electronic Medical Record (EMR) field.

scholarly journals Privacy and User Experience in 21st Century Library Discovery

2017 ◽  
Vol 36 (2) ◽  
pp. 48-58 ◽  
Author(s):  
Shayna Pekala
Keyword(s):  
User Experience ◽  
Search Engines ◽  
Web Search ◽  
New Wave ◽  
User Privacy ◽  
Future Design ◽  
User Data ◽  
Online Tracking ◽  
Web Search Engines ◽  
Data Libraries

Over the last decade, libraries have taken advantage of emerging technologies to provide new discovery tools to help users find information and resources more efficiently. In the wake of this technological shift in discovery, privacy has become an increasingly prominent and complex issue for libraries. The nature of the web, over which users interact with discovery tools, has substantially diminished the library’s ability to control patron privacy. The emergence of a data economy has led to a new wave of online tracking and surveillance, in which multiple third parties collect and share user data during the discovery process, making it much more difficult, if not impossible, for libraries to protect patron privacy. In addition, users are increasingly starting their searches with web search engines, diminishing the library’s control over privacy even further.While libraries have a legal and ethical responsibility to protect patron privacy, they are simultaneously challenged to meet evolving user needs for discovery. In a world where “search” is synonymous with Google, users increasingly expect their library discovery experience to mimic their experience using web search engines. However, web search engines rely on a drastically different set of privacy standards, as they strive to create tailored, personalized search results based on user data. Libraries are seemingly forced to make a choice between delivering the discovery experience users expect and protecting user privacy. This paper explores the competing interests of privacy and user experience, and proposes possible strategies to address them in the future design of library discovery tools.

A Semantic-Based Dynamic Search Engine Design and Implementation for Electronic Medical Records

2010 ◽  
Vol 1 (2) ◽  
pp. 61-73
Author(s):  
Weider D. Yu ◽  
Seshadri K. Yilayavilli
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
First Generation ◽  
Web Search ◽  
Relevant Information ◽  
Engine Design ◽  
Search Service ◽  
Web Search Engines ◽  
World Information ◽  
The Right

In the current technology driven world, information retrieval activities are in almost every aspect of daily, as society uses popular web search engines like Google, Yahoo!, Live Search, Ask, and so forth to obtain helpful information. Often, these popular search engines look for and obtain key information; however, not all of the retrieved items are relevant in context to the search target a. Thus, it is left for the user to filter out unwanted information, using only a few information items left from the search results. These popular web search engines use a first generation search service based on “static keywords”, which require the users to know exactly what they want to search and enter the right keywords. This approach puts the user at a disadvantage. In this paper, the authors investigate and design a dynamic, question-answer search engine that enables searching by attributes for more precise and relevant information in Electronic Medical Record (EMR) field.

A branding model for web search engines

2012 ◽  
Vol 7 (3) ◽  
pp. 195 ◽  
Author(s):  
Lu Zhang ◽  
Bernard J. Jansen ◽  
Anna S. Mattila
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Web Search Engines

scholarly journals Web Search Engines - Not Yet a Reliable Replacement for Bibliographic Databases

2018 ◽  
Vol 13 (3) ◽  
pp. 85-87
Author(s):  
Emma Hughes
Keyword(s):  
Social Sciences ◽  
Social Services ◽  
Search Engines ◽  
English Language ◽  
Web Search ◽  
Boolean Logic ◽  
Bibliographic Databases ◽  
Quality Of Reporting ◽  
Health And Social Care ◽  
Web Search Engines

A Review of: Bates, J., Best, P., McQuilkin, J., & Taylor, B. (2017) Will web search engines replace bibliographic databases in the systematic identification of research? The Journal of Academic Librarianship, 43(1), 8-17. https://doi.org/10.1016/j.acalib.2016.11.003 Abstract Objective - To explore whether web search engines could replace bibliographic databases in retrieving research. Design - Systematic review. Setting - English language articles in health and social care; comparing bibliographic databases and web search engines for retrieving research published between January 2005 and August 2015, in peer-reviewed journals and available in full-text. Subjects - Eight bibliographic databases: ASSIA (Applied Social Sciences Index and Abstracts), CINAHL Plus (Cumulative Index to Nursing and Allied Health Literature), LISA (Library and Information Science Abstracts), Medline, PsycInfo, Scopus, SSA (Social Services Abstracts), and SSCI (Social Sciences Citation Index) and five web search engines: Ask, Bing, Google, Google Scholar, Yahoo. Methods - A literature search via the above bibliographic databases and web search engines. The retrieved results were independently appraised by two researchers, using a combination of tools and checklists, including the PRESS checklist (McGowan et al., 2016) and took guidance on developing search strategies from the Centre for Reviews and Dissemination (2009). Main Results - Sixteen papers met the appraisal requirements. Each paper compared at least one bibliographic database against one web-search engine. The authors also discuss findings from their own search process. Precision and sensitivity scores from each paper were compared. The results highlighted that web search engines do not necessarily use Boolean logic and in general have limited functionality compared to bibliographic databases. There were variances in the way precision scores were calculated between papers, but when based on the first 100 results, web search engines were similar to some databases. However, their sensitivity scores were much weaker. Conclusion - Whilst precision scores were strong for web search engines, sensitivity was lacking; therefore web search engines cannot be seen as a replacement for bibliographic databases at this time. The authors recommend improving the quality of reporting in studies regarding literature searching in academia in order for reliable comparisons to be made.

scholarly journals Analysis of Haptics Evolution from Web Search Engines’ Data

2009 ◽  
Vol 4 (4) ◽  
Author(s):  
Agnès Guerraz ◽  
Céline Loscos
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Web Search Engines
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