As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

Based on a survey conducted in 2019 among internet users in the United States, the majority of adults (36 percent) admitted they would switch search engines if it meant getting better quality results. Furthermore, 33.7 percent stated that knowing their data was not being collected by a platform would also encourage them to make the switch. Other factors listed included 'having fewer ads' and a well designed interface. Overall, there was a noticeable lean toward search result quality and data privacy when it came to search engine selection.

Google leads despite user preference for increased privacy

Despite a strong consumer call for data protection, Google topped the list when it came to search engines with 93 percent of Americans surveyed reporting to having used the popular search giant at some point during the past 4 weeks. In comparison, the second most popular platform Yahoo! had only been used by 31 percent of those surveyed. Meanwhile DuckDuckGo, the search engine most known for protecting user data and search history had only been used by 8 percent. Mobile search figures lean even more in Google's favor. Here, a similar share (93 percent) of the market as of January 2021 belonged to Google, while approximately 3 percent was held by DuckDuckGo.

Growth expected for search advertising

With search engines playing a significant role in internet use be it on desktop or mobile, companies and search platforms alike are seeing an increased opportunity in the field of search engine advertising. Nationwide spend in the industry reached an impressive 58.2 billion U.S. dollars in 2020, and was forecast to further rise to 66.2 billion within the following year.

Search Engine Market was valued at USD 203.1 Billion in 2023 and is projected to reach USD 478.9 Billion by 2031, growing at a CAGR of 10% during the forecast period 2026-2032.Global Search Engine Market DriversThe market drivers for the Search Engine Market can be influenced by various factors. These may include:Growth in Internet Penetration: Increase in internet accessibility worldwide, with more individuals and businesses going online.Rising Mobile Device Usage: Surge in smartphone and tablet usage, leading to more searches conducted via mobile devices.E-commerce Expansion: Growth in online shopping boosts search engine usage as consumers look for products and services online.Technological Advancements: Innovations in artificial intelligence (AI), machine learning, and natural language processing enhance search engine functionalities.Marketing and Advertising Needs: Increased demand for digital marketing and search engine optimization (SEO) as companies seek to improve online visibility.Big Data Analytics: Use of big data to refine search algorithms and provide more personalized search results.Voice Search and Virtual Assistants: Rising popularity of voice-activated searches through devices like Amazon Echo and Google Home.Local Search Optimization: Growth in localized searches as businesses focus on targeting specific geographic areas.Content Digitalization: Increasing volumes of digital content available on the internet, making search engines critical tools for information retrieval.Improvement in User Experience: Enhanced user interfaces and faster search results improve user satisfaction and drive more frequent usage.

Feature comparison matrix of Google alternative search engines

Search Engine Market Outlook

The search engine market size was valued at approximately USD 124 billion in 2023 and is projected to reach USD 258 billion by 2032, witnessing a robust CAGR of 8.5% during the forecast period. This growth is largely attributed to the increasing reliance on digital platforms and the internet across various sectors, which has necessitated the use of search engines for data retrieval and information dissemination. With the proliferation of smartphones and the expansion of internet access globally, search engines have become indispensable tools for both businesses and consumers, driving the market's upward trajectory. The integration of artificial intelligence and machine learning technologies into search engines is transforming the way search engines operate, offering more personalized and efficient search results, thereby further propelling market growth.

One of the primary growth factors in the search engine market is the ever-increasing digitalization across industries. As businesses continue to transition from traditional modes of operation to digital platforms, the need for search engines to navigate and manage data becomes paramount. This shift is particularly evident in industries such as retail, BFSI, and healthcare, where vast amounts of data are generated and require efficient management and retrieval systems. The integration of AI and machine learning into search engine algorithms has enhanced their ability to process and interpret large datasets, thereby improving the accuracy and relevance of search results. This technological advancement not only improves user experience but also enhances the competitive edge of businesses, further fueling market growth.

Another significant growth factor is the expanding e-commerce sector, which relies heavily on search engines to connect consumers with products and services. With the rise of e-commerce giants and online marketplaces, consumers are increasingly using search engines to find the best prices, reviews, and availability of products, leading to a surge in search engine usage. Additionally, the implementation of voice search technology and the growing popularity of smart home devices have introduced new dynamics to search engine functionality. Consumers are now able to conduct searches verbally, which has necessitated the adaptation of search engines to incorporate natural language processing capabilities, further driving market growth.

The advertising and marketing sectors are also contributing significantly to the growth of the search engine market. Businesses are leveraging search engines as a primary tool for online advertising, given their wide reach and ability to target specific audiences. Pay-per-click advertising and search engine optimization strategies have become integral components of digital marketing campaigns, enabling businesses to enhance their visibility and engagement with potential customers. The measurable nature of these advertising techniques allows businesses to assess the effectiveness of their campaigns and make data-driven decisions, thereby increasing their reliance on search engines and contributing to overall market growth.

The evolution of search engines is closely tied to the development of Ai Enterprise Search, which is revolutionizing how businesses access and utilize information. Ai Enterprise Search leverages artificial intelligence to provide more accurate and contextually relevant search results, making it an invaluable tool for organizations that manage large volumes of data. By understanding user intent and learning from past interactions, Ai Enterprise Search systems can deliver personalized experiences that enhance productivity and decision-making. This capability is particularly beneficial in sectors such as finance and healthcare, where quick access to precise information is crucial. As businesses continue to digitize and data volumes grow, the demand for Ai Enterprise Search solutions is expected to increase, further driving the growth of the search engine market.

Regionally, North America holds a significant share of the search engine market, driven by the presence of major technology companies and a well-established digital infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the rapid digital transformation in emerging economies such as China and India, where increasing internet penetration and smartphone adoption are driving demand for search engines. Additionally, government initiatives to

In January 2025, Google remained by far the most popular search engine in the UK, holding a market share of 93.25 percent across all devices. That month, Bing had a market share of approximately 4.22 percent in second place, followed by Yahoo! with approximately 1.37 percent. The EU vs Google Despite Google’s dominance of the search engine market, maintaining its position at the top has not been a smooth ride. Google’s market share saw a decline in the summer of 2018, plummeting to an all-time-low in July. The search engine experienced a similar dip in June and July 2017. These two low points coincided with the European Commission’s antitrust charges against the company, both of which were unprecedented in the now decade-long duel between both parties. As skepticism towards search engine platforms grows in line with public concern regarding censorship and data privacy, alternative services like Duckduckgo offer users both information protection and unfiltered results. Despite this, it still held less than one percent of the industry’s market share as of June 2021. Perception of fake news in the UK According to a questionnaire conducted in the United Kingdom in 2018, 57.7 percent of respondents had come across inaccurate news on social media at least once before. Rising concerns over fake news, or information which has been manipulated to influence the public has been a hot topic in recent years. The younger generation however, remains skeptical with nearly half of Generation Z claiming to be either unconcerned about fake news, or believed that it did not exist altogether.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

We present Qbias, two novel datasets that promote the investigation of bias in online news search as described in

Fabian Haak and Philipp Schaer. 2023. 𝑄𝑏𝑖𝑎𝑠 - A Dataset on Media Bias in Search Queries and Query Suggestions. In Proceedings of ACM Web Science Conference (WebSci’23). ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3578503.3583628.

Dataset 1: AllSides Balanced News Dataset (allsides_balanced_news_headlines-texts.csv)

The dataset contains 21,747 news articles collected from AllSides balanced news headline roundups in November 2022 as presented in our publication. The AllSides balanced news feature three expert-selected U.S. news articles from sources of different political views (left, right, center), often featuring spin bias, and slant other forms of non-neutral reporting on political news. All articles are tagged with a bias label by four expert annotators based on the expressed political partisanship, left, right, or neutral. The AllSides balanced news aims to offer multiple political perspectives on important news stories, educate users on biases, and provide multiple viewpoints. Collected data further includes headlines, dates, news texts, topic tags (e.g., "Republican party", "coronavirus", "federal jobs"), and the publishing news outlet. We also include AllSides' neutral description of the topic of the articles.
Overall, the dataset contains 10,273 articles tagged as left, 7,222 as right, and 4,252 as center.

To provide easier access to the most recent and complete version of the dataset for future research, we provide a scraping tool and a regularly updated version of the dataset at https://github.com/irgroup/Qbias. The repository also contains regularly updated more recent versions of the dataset with additional tags (such as the URL to the article). We chose to publish the version used for fine-tuning the models on Zenodo to enable the reproduction of the results of our study.

Dataset 2: Search Query Suggestions (suggestions.csv)

The second dataset we provide consists of 671,669 search query suggestions for root queries based on tags of the AllSides biased news dataset. We collected search query suggestions from Google and Bing for the 1,431 topic tags, that have been used for tagging AllSides news at least five times, approximately half of the total number of topics. The topic tags include names, a wide range of political terms, agendas, and topics (e.g., "communism", "libertarian party", "same-sex marriage"), cultural and religious terms (e.g., "Ramadan", "pope Francis"), locations and other news-relevant terms. On average, the dataset contains 469 search queries for each topic. In total, 318,185 suggestions have been retrieved from Google and 353,484 from Bing.

The file contains a "root_term" column based on the AllSides topic tags. The "query_input" column contains the search term submitted to the search engine ("search_engine"). "query_suggestion" and "rank" represents the search query suggestions at the respective positions returned by the search engines at the given time of search "datetime". We scraped our data from a US server saved in "location".

We retrieved ten search query suggestions provided by the Google and Bing search autocomplete systems for the input of each of these root queries, without performing a search. Furthermore, we extended the root queries by the letters a to z (e.g., "democrats" (root term) >> "democrats a" (query input) >> "democrats and recession" (query suggestion)) to simulate a user's input during information search and generate a total of up to 270 query suggestions per topic and search engine. The dataset we provide contains columns for root term, query input, and query suggestion for each suggested query. The location from which the search is performed is the location of the Google servers running Colab, in our case Iowa in the United States of America, which is added to the dataset.

AllSides Scraper

At https://github.com/irgroup/Qbias, we provide a scraping tool, that allows for the automatic retrieval of all available articles at the AllSides balanced news headlines.

We want to provide an easy means of retrieving the news and all corresponding information. For many tasks it is relevant to have the most recent documents available. Thus, we provide this Python-based scraper, that scrapes all available AllSides news articles and gathers available information. By providing the scraper we facilitate access to a recent version of the dataset for other researchers.

BackgroundThis study aimed to investigate the quality and readability of online English health information about dental sensitivity and how patients evaluate and utilize these web-based information.MethodsThe credibility and readability of health information was obtained from three search engines. We conducted searches in "incognito" mode to reduce the possibility of biases. Quality assessment utilized JAMA benchmarks, the DISCERN tool, and HONcode. Readability was analyzed using the SMOG, FRE, and FKGL indices.ResultsOut of 600 websites, 90 were included, with 62.2% affiliated with dental or medical centers, among these websites, 80% exclusively related to dental implant treatments. Regarding JAMA benchmarks, currency was the most commonly achieved and 87.8% of websites fell into the "moderate quality" category. Word and sentence counts ranged widely with a mean of 815.7 (±435.4) and 60.2 (±33.3), respectively. FKGL averaging 8.6 (±1.6), SMOG scores averaging 7.6 (±1.1), and FRE scale showed a mean of 58.28 (±9.1), with "fair difficult" being the most common category.ConclusionThe overall evaluation using DISCERN indicated a moderate quality level, with a notable absence of referencing. JAMA benchmarks revealed a general non-adherence among websites, as none of the websites met all of the four criteria. Only one website was HON code certified, suggesting a lack of reliable sources for web-based health information accuracy. Readability assessments showed varying results, with the majority being "fair difficult". Although readability did not significantly differ across affiliations, a wide range of the number of words and sentences count was observed between them.

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

Nowadays web portals play an essential role in searching and retrieving information in the several fields of knowledge: they are ever more technologically advanced and designed for supporting the storage of a huge amount of information in natural language originating from the queries launched by users worldwide. A good example is given by the WorldWideScience search engine: The database is available at http://worldwidescience.org/. It is based on a similar gateway, Science.gov, which is the major path to U.S. government science information, as it pulls together Web-based resources from various agencies. The information in the database is intended to be of high quality and authority, as well as the most current available from the participating countries in the Alliance, so users will find that the results will be more refined than those from a general search of Google. It covers the fields of medicine, agriculture, the environment, and energy, as well as basic sciences. Most of the information may be obtained free of charge (the database itself may be used free of charge) and is considered ‘‘open domain.’’ As of this writing, there are about 60 countries participating in WorldWideScience.org, providing access to 50+databases and information portals. Not all content is in English. (Bronson, 2009) Given this scenario, we focused on building a corpus constituted by the query logs registered by the GreyGuide: Repository and Portal to Good Practices and Resources in Grey Literature and received by the WorldWideScience.org (The Global Science Gateway) portal: the aim is to retrieve information related to social media which as of today represent a considerable source of data more and more widely used for research ends. This project includes eight months of query logs registered between July 2017 and February 2018 for a total of 445,827 queries. The analysis mainly concentrates on the semantics of the queries received from the portal clients: it is a process of information retrieval from a rich digital catalogue whose language is dynamic, is evolving and follows – as well as reflects – the cultural changes of our modern society.

AI in Search Engines Market Outlook

As per our latest research, the global AI in Search Engines market size reached USD 8.2 billion in 2024, demonstrating robust momentum driven by the rapid adoption of artificial intelligence technologies across digital platforms. The market is expected to expand at a CAGR of 23.5% from 2025 to 2033, with the market size projected to reach USD 65.7 billion by 2033. This remarkable growth is primarily attributed to the escalating demand for enhanced search experiences, intelligent content discovery, and the integration of AI-powered features such as natural language processing and machine learning in both consumer and enterprise environments.

The accelerating proliferation of digital content, coupled with the increasing complexity of user queries, is a significant growth factor propelling the adoption of AI in Search Engines. Businesses and consumers alike are seeking more accurate, context-aware, and personalized search results, which traditional keyword-based algorithms struggle to provide. AI-driven search engines leverage advanced techniques such as deep learning and natural language processing to understand user intent, semantic context, and deliver highly relevant results. This trend is further amplified by the rise of voice-activated assistants, mobile search, and the demand for multi-modal search capabilities, all of which require sophisticated AI algorithms to function effectively.

Another critical driver for the AI in Search Engines market is the increasing emphasis on user experience and engagement. Enterprises are investing in AI-powered search technologies to improve website navigation, enhance e-commerce product discovery, and boost customer satisfaction. AI enables search engines to offer predictive suggestions, auto-complete, and personalized recommendations, leading to higher conversion rates and deeper user engagement. Additionally, the integration of AI in enterprise search platforms is helping organizations unlock value from unstructured data, streamline knowledge management, and support data-driven decision-making processes.

The rapid advancements in AI technologies, particularly in machine learning, deep learning, and computer vision, are significantly expanding the capabilities of modern search engines. These technologies enable the processing of diverse data types, including text, images, audio, and video, thus supporting a wide range of search applications beyond traditional web search. Furthermore, the growing investments in AI research and the increasing availability of cloud-based AI services are lowering the barriers to adoption, enabling even small and medium enterprises to harness the power of AI in their search solutions. This democratization of AI technology is expected to further fuel market growth in the coming years.

From a regional perspective, North America remains the largest market for AI in Search Engines, accounting for the highest revenue share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology companies, advanced digital infrastructure, and early adoption of AI technologies are key factors driving market growth in these regions. Meanwhile, Asia Pacific is emerging as the fastest-growing market, supported by rapid digitization, increasing internet penetration, and substantial investments in AI research and development. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace AI-driven digital transformation.

Component Analysis

The AI in Search Engines market is segmented by component into Software, Hardware, and Services. The software segment dominates the market, accounting for the largest share in 2024. This dominance is attributed to the critical role of AI algorithms, frameworks, and platforms in powering intelligent search functionalities. AI software solutions encompass a wide range of technologies, including natural language processing engines, machine learning models, and deep learning frameworks, which are essential for understanding complex user queries, extracting insights from vast datasets, and delivering relevant search results. The continuous evolution of AI software, with frequent updates and innovations, ensures that search engines remain at the forefront of digital transformation.

The hardware segment, while smaller in comparison,

BackgroundThe existing literature has not examined how Chinese direct-to-consumer (DTC) genetic testing providers navigate the issues of informed consent, privacy, and data protection associated with testing services. This research aims to explore these questions by examining the relevant documents and messages published on websites of the Chinese DTC genetic test providers.MethodsUsing Baidu.com, the most popular Chinese search engine, we compiled the websites of providers who offer genetic testing services and analyzed available documents related to informed consent, the terms of services, and the privacy policy. The analyses were guided by the following inquiries as they applied to each DTC provider: the methods available for purchasing testing products; the methods providers used to obtain informed consent; privacy issues and measures for protecting consumers’ health information; the policy for third-party data sharing; consumers right to their data; and the liabilities in the event of a data breach.Results68.7% of providers offer multiple channels for purchasing genetic testing products, and that social media has become a popular platform to promote testing services. Informed consent forms are not available on 94% of providers’ websites and a privacy policy is only offered by 45.8% of DTC genetic testing providers. Thirty-nine providers stated that they used measures to protect consumers’ information, of which, 29 providers have distinguished consumers’ general personal information from their genetic information. In 33.7% of the cases examined, providers stated that with consumers’ explicit permission, they could reuse and share the clients’ information for non-commercial purposes. Twenty-three providers granted consumer rights to their health information, with the most frequently mentioned right being the consumers’ right to decide how their data can be used by providers. Lastly, 21.7% of providers clearly stated their liabilities in the event of a data breach, placing more emphasis on the providers’ exemption from any liability.ConclusionsCurrently, the Chinese DTC genetic testing business is running in a regulatory vacuum, governed by self-regulation. The government should develop a comprehensive legal framework to regulate DTC genetic testing offerings. Regulatory improvements should be made based on periodical reviews of the supervisory strategy to meet the rapid development of the DTC genetic testing industry.

A large-scale video benchmark for video understanding, especially the emerging task of translating video to text. This is achieved by collecting 257 popular queries from a commercial video search engine, with 118 videos for each query. In its current version, MSR-VTT provides 10K web video clips with 41.2 hours and 200K clip-sentence pairs in total, covering the most comprehensive categories and diverse visual content, and representing the largest dataset in terms of sentence and vocabulary. Each clip is annotated with about 20 natural sentences by 1,327 AMT workers.

HealthSearchQA

Dataset of consumer health questions released by Google for the Med-PaLM paper (arXiv preprint). From the paper: We curated our own additional dataset consisting of 3,173 commonly searched consumer questions, referred to as HealthSearchQA. The dataset was curated using seed medical conditions and their associated symptoms. We used the seed data to retrieve publicly-available commonly searched questions generated by a search engine, which were displayed to all users… See the full description on the dataset page: https://huggingface.co/datasets/aisc-team-d2/healthsearchqa.

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

The replication package for our article The State of Serverless Applications: Collection,Characterization, and Community Consensus provides everything required to reproduce all results for the following three studies:

• Serverless Application Collection
• Serverless Application Characterization
• Comparison Study

Serverless Application Collection

We collect descriptions of serverless applications from open-source projects, academic literature, industrial literature, and scientific computing.

Open-source Applications

As a starting point, we used an existing data set on open-source serverless projects from this study. We removed small and inactive projects based on the number of files, commits, contributors, and watchers. Next, we manually filtered the resulting data set to include only projects that implement serverless applications. We provide a table containing all projects that remained after the filtering alongside the notes from the manual filtering.

Academic Literature Applications

We based our search on an existing community-curated dataset on literature for serverless computing consisting of over 180 peer-reviewed articles. First, we filtered the articles based on title and abstract. In a second iteration, we filtered out any articles that implement only a single function for evaluation purposes or do not include sufficient detail to enable a review. As the authors were familiar with some additional publications describing serverless applications, we contributed them to the community-curated dataset and included them in this study. We provide a table with our notes from the manual filtering.

Scientific Computing Applications

Most of these scientific computing serverless applications are still at an early stage and therefore there is little public data available. One of the authors is employed at the German Aerospace Center (DLR) at the time of writing, which allowed us to collect information about several projects at DLR that are either currently moving to serverless solutions or are planning to do so. Additionally, an application from the German Electron Synchrotron (DESY) could be included. For each of these scientific computing applications, we provide a document containing a description of the project and the names of our contacts that provided information for the characterization of these applications.

• SC1 Copernicus Sentinel-1 for near-real-time water monitoring
• SC2 Reprocessing Sentinel 5 Precursor data with ProEO
• SC3 High-Performance Data Analytics for Earth Observation
• SC4 Tandem-L exploitation platform
• SC5 Global Urban Footprint
• SC6 DESY - High Throughput Data Taking

Collection of serverless applications

Based on the previously described methodology, we collected a diverse dataset of 89 serverless applications from open-source projects, academic literature, industrial literature, and scientific computing. This dataset is can be found in Dataset.xlsx.

Serverless Application Characterization

As previously described, we collected 89 serverless applications from four different sources. Subsequently, two randomly assigned reviewers out of seven available reviewers characterized each application along 22 characteristics in a structured collaborative review sheet. The characteristics and potential values were defined a priori by the authors and iteratively refined, extended, and generalized during the review process. The initial moderate inter-rater agreement was followed by a discussion and consolidation phase, where all differences between the two reviewers were discussed and resolved. The six scientific applications were not publicly available and therefore characterized by a single domain expert, who is either involved in the development of the applications or in direct contact with the development team.

Initial Ratings & Interrater Agreement Calculation

The initial reviews are available as a table, where every application is characterized along with the 22 characteristics. A single value indicates that both reviewers assigned the same value, whereas a value of the form [Reviewer 2] A | [Reviewer 4] B indicates that for this characteristic, reviewer two assigned the value A, whereas reviewer assigned the value B.

Our script for the calculation of the Fleiß-Kappa score based on this data is also publically available. It requires the python package pandas and statsmodels. It does not require any input and assumes that the file Initial Characterizations.csv is located in the same folder. It can be executed as follows:

python3 CalculateKappa.py

Results Including Unknown Data

In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find.

For six characteristics, many applications were assigned the ''Unknown'' value, i.e., the reviewers were not able to determine the value of this characteristic. Therefore, we excluded these characteristics from this study. For the remaining characteristics, the percentage of ''Unknowns'' ranges from 0–19% with two outliers at 25% and 30%. These ''Unknowns'' were excluded from the percentage values presented in the article. As part of our replication package, we provide the raw results for each characteristic including the ''Unknown'' percentages in the form of bar charts.

The script for the generation of these bar charts is also part of this replication package). It uses the python packages pandas, numpy, and matplotlib. It does not require any input and assumes that the file Dataset.csv is located in the same folder. It can be executed as follows:

python3 GenerateResultsIncludingUnknown.py

Final Dataset & Figure Generation

In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find. Following this process, we were able to resolve all conflicts, resulting in a collection of 89 applications described by 18 characteristics. This dataset is available here: link

The script to generate all figures shown in the chapter "Serverless Application Characterization can be found here. It does not require any input but assumes that the file Dataset.csv is located in the same folder. It uses the python packages pandas, numpy, and matplotlib. It can be executed as follows:

python3 GenerateFigures.py

Comparison Study

To identify existing surveys and datasets that also investigate one of our characteristics, we conducted a literature search using Google as our search engine, as we were mostly looking for grey literature. We used the following search term:

("serverless" OR "faas") AND ("dataset" OR "survey" OR "report") after: 2018-01-01

This search term looks for any combination of either serverless or faas alongside any of the terms dataset, survey, or report. We further limited the search to any articles after 2017, as serverless is a fast-moving field and therefore any older studies are likely outdated already. This search term resulted in a total of 173 search results. In order to validate if using only a single search engine is sufficient, and if the search term is broad enough, we

The transition from analog to digital archives and the recent explosion of online content offers researchers novel ways of engaging with data. The crucial question for ensuring a balance between the supply and demand-side of data, is whether this trend connects to existing scholarly practices and to the average search skills of researchers. To gain insight into this process a survey was conducted among nearly three hundred (N= 288) humanities scholars in the Netherlands and Belgium with the aim of finding answers to the following questions: 1) To what extent are digital databases and archives used? 2) What are the preferences in search functionalities 3) Are there differences in search strategies between novices and experts of information retrieval? Our results show that while scholars actively engage in research online they mainly search for text and images. General search systems such as Google and JSTOR are predominant, while large-scale collections such as Europeana are rarely consulted. Searching with keywords is the dominant search strategy and advanced search options are rarely used. When comparing novice and more experienced searchers, the first tend to have a more narrow selection of search engines, and mostly use keywords. Our overall findings indicate that Google is the key player among available search engines. This dominant use illustrates the paradoxical attitude of scholars toward Google: while transparency of provenance and selection are deemed key academic requirements, the workings of the Google algorithm remain unclear. We conclude that Google introduces a black box into digital scholarly practices, indicating scholars will become increasingly dependent on such black boxed algorithms. This calls for a reconsideration of the academic principles of provenance and context.

The Search Engine industry is highly concentrated, with three companies controlling almost the entire industry; the largest company, Alphabet Inc., has a market share greater than 96%. Search engines provide web portals that generate and maintain extensive databases of internet addresses. Industry companies generate most, if not all, of their revenue from advertising. Technological growth has resulted in more households being connected to the Internet and a boom in e-commerce has made the industry increasingly innovative. A climb in the proportion of households with internet access has supported revenue growth, while expanding technological integration with daily life has boosted demand for web search. A greater proportion of transactions being carried out online has driven innovation in targeted digital advertising, with declines in rival advertising formats like print media and television expanding the focus on digital marketing as a core strategy. Industry revenue is expected to jump at a compound annual rate of 3.8%, to reach £5.4 billion over the five years through 2025-26. Revenue is forecast to climb by 3.5% in 2025-26. Industry profit has remained high and expanded alongside a surge in search and display advertising, with total UK digital ad spend. The rise of the mobile advertising market and the proliferation of mobile devices mean there are plenty of opportunities for search engines, which are expected to capitalise on these trends further moving forward. While continued growth in localised digital marketing and rising overall UK marketing budgets are set to propel industry revenues, Google faces mounting regulatory scrutiny. The Digital Markets, Competition and Consumers Act 2024, with the impending Strategic Market Status designation for Google, is poised to shake up the landscape by curtailing Google’s market power and fostering greater transparency. Search engines will need to innovate to fend off rising competition from social media platforms, which are attracting advertisers through advanced targeting capabilities. Although niche, privacy-centric search engines could capture incremental market share as consumer privacy concerns intensify, the industry’s overwhelming concentration, with Google’s unmatched user base and ad inventory, means transformative change will likely be incremental. Nonetheless, technological advancements that incorporate user data are anticipated to make it easier to tailor advertisements and develop new ways of using consumer data. Industry revenue is forecast to jump at a compound annual rate of 5.9% over the five years through 2030-31, to reach £7.2 billion.