Neural Networks in Big Data and Web Search

Serrano, Will

doi:10.3390/data4010007

Cited by 26 publications

(18 citation statements)

References 151 publications

(148 reference statements)

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“…Today this topic is attracting more attention [91] and can be divided into onsite and offsite factors [92]. Onsite factors are domain-, website-, and page-related [93].…”

Section: Introductionmentioning

confidence: 99%

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Strzelecki

2020

IJERPH

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The Google search engine answers many health and medical information queries every day. People have become used to searching for this type of information. This paper presents a study which examined the visibility of health and medical information websites. The purpose of this study was to find out why Google is decreasing the visibility of such websites and how to measure this decrease. Since August 2018, Google has been more rigorously rating these websites, since they can potentially impact people’s health. The method of the study was to collect data about the visibility of health and medical information websites in sequential time snapshots. Visibility consists of combined data of unique keywords, positions, and URL results. The sample under study was made up of 21 websites selected from 10 European countries. The findings reveal that in sequential time snapshots, search visibility decreased. The decrease was not dependent on the country or the language. The main reason why Google is decreasing the visibility of such websites is that they do not meet high ranking criteria.

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“…Today this topic is attracting more attention [91] and can be divided into onsite and offsite factors [92]. Onsite factors are domain-, website-, and page-related [93].…”

Section: Introductionmentioning

confidence: 99%

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Strzelecki

2020

IJERPH

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“…Governance for the Data Marketplace shall comprise support, sponsorship, training and development about ethical data issues and the consequences around the use or misuse of data. Data recommendations within the Data Marketplace shall be relevant to the users needs, rather than biased by economic interest from the data owners or the Data Marketplace service provider [38]. Big data present challenges for human users as it cannot manually processed or assimilated.…”

Section: Ethics In the Real Estate Data Marketplacementioning

confidence: 99%

Real Estate Data Marketplace

et al. 2021

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Real estate financing, construction, and management are being revolutionised by the data science and infrastructure technologies of Artificial Intelligence (AI), Internet of Things (IoT), Big Data, Digital Object Identifiers (DOI) and Blockchain.The key to automation and integration is a Real Estate Data Marketplace across the lifespan of finance, planning, construction, regulation, management, even refurbishment and demolition. Integration of digital 4.0 and industry 4.0. Real estate is becoming increasingly digital, automated and integrated; embracing connected-buildings, wearable personal devices, integrated transportation systems, buildings and infrastructure data management and smart cities. The key to promoting the use of data-driven techniques in the real estate sector is the introduction of a Data Marketplace that promotes validation of data and tokenisation to create value for the owners of the data, Blockchain security and governance for users of the data, the application of Artificial Intelligence for data analytics and ethics for governance. The technical capability exists today for a complete end-to-end reshaping of the real estate market based on data and ethics. This is data-driven real estate (cf. Google, Amazon, Alibaba, Tencent). The contributions of this review paper are fourfold: (a) to provide a vision of a fully digital real estate marketplace; (b) to brief the industry on digital technology and also stimulate engagement with the computer science community; (c) to encourage debate amongst professionals, academics and politicians of the benefits of full automation and integration of real estate; (d) present ethics for the Real Estate Data Marketplace based on lawfulness, fairness and transparency.

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“…Currently, web search engines use different ranking factors for websites to determine their position on a results page. Today this topic is attracting more attention [11] and can be divided into onsite and offsite factors [12]. Onsite factors are domain-, website-, and page-related [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Direct Answers in Google Search Results

Strzelecki

Rutecka

2020

IEEE Access

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This study aims to explore Google's new "featured snippet" (a direct answer that appears in web search results) to better understand the method of extracting and displaying such answers above regular ranked results. The motivation for the study was to investigate form, structure, and relation to query, content, and domain of the direct answer. This study utilized a dataset of 743,798 keywords and the displayed direct answers. It also included web search data for every direct answer, e.g. resulting domain, full URL address and its original ranking, content of the direct answer, and competition (measured as the number of monthly searches for every query). The main finding concerns the form of construction of keywords used in the website content. Keywords should be built in the form of short, two-to-four-word sentences comprising the subject and its attribute. Using relative pronouns, articles, and prepositions, as well as using questions as queries, can help to properly define a query and display the best direct answer. The dataset is relatively small compared to the volume of searches made daily. No other factors were extracted from the URL, and all data concerned the Google search engine only. Implications for webmasters include: keywords used in website content should be close to grammar forms used in queries; use keywords in URLs; write a comprehensive introduction section on the webpage; and use tables and lists HTML markup. The main objective of this study was to examine the structure of the user's queries which caused in appearing of direct answers. It is comprehensive and based on real data used by web search engine users. The data used to support the findings have been deposited in the Zenodo repository (

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Neural Networks in Big Data and Web Search

Cited by 26 publications

References 151 publications

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Real Estate Data Marketplace

Direct Answers in Google Search Results

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