In February 2025, Verizon Media processed 2.51 million online search queries in the United States, and market leader Google processed over 14.7 million search queries. In total, Google accounted for 60 percent of the U.S. desktop search queries. Google was also the leading mobile search provider in the United States, accounting for around 95 percent of the market as of March 2024.

As of December 2024, around 877.82 million people in China used search engines, reaching a record high. This represented 79.2 percent of the total internet user base.

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.

Search engines, which collect, organize and display knowledge of the internet, are the backbone of the information age and have helped popularize the ad-supported attention economy that prevails throughout the internet. From 2019 to 2024, spending on internet advertising has maintained strong momentum as consumer demand for internet access continued to surge, driven by the adoption of LTE, 5G and unlimited mobile data plans. Despite COVID-19 depressing total advertising expenditure, digital advertising continued to grow as consumers practically lived online while stay-at-home orders were in place. As a result, search engine revenue from advertising is slated to mount at a CAGR of 10.4% to $287.5 billion, including an anticipated hike of 8.4% in 2024, with profit at 18.7%. The search engine industry is fundamentally differentiated from the rest of the economy by its advertising sales framework, market aggregation and high interconnection with other industries. While search is a consumer product, search revenue comes from a platform's desirability to advertisers, not users. Search platforms must balance providing the best search experience while integrating as many advertisements as possible. This difficult balance is challenging to achieve because advertising dollars tend to scale best on the leading search platform, increasing aggregation forces for search providers. The market leaders in search, Google and Microsoft, have met this balance by using advertising revenue to grow a suite of services designed to collect extensive behavior information on and off the search website. This data then targets ads to hyper-specific markets, funding the search business model. As the number of hours spent on the internet continues to mount, search engine revenue is poised to climb at a CAGR of 7.1% to $404.9 billion through the end of 2029. Advertisers will rely increasingly on search engine marketing due to its cost-effectiveness and efficiency advantages over traditional media. With proper analytics software installed, marketers can track which terms, advertisements and websites are the most effective, enabling incremental real-time tweaks and improvements in advertising campaigns. Artificial intelligence has promised to change the purpose of search from navigation to finding answers, which will change the structure of the internet, just as search engine providers have done many times before.

This statistic shows the number of mobile search users in China from 2011 to 2020. By the end of 2020, over 768 million people in China used mobile phones to search the web, representing 78 percent of the total mobile users.

Percentage data of the number of searches: We investigated the transition of people searching for the keywords "university students", "distance learning" and "online classes" using Google search. (Number of searches) ÷ base value is a relative value, i.e. the data with the highest number of searches in the data is 100%, and the other data are calculated as a percentage as (data) ÷ (most frequent data).

United States Google Search Trends: Government Measures: Government Subsidy data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. United States Google Search Trends: Government Measures: Government Subsidy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 0.000 Score in 14 May 2025 and a record low of 0.000 Score in 14 May 2025. United States Google Search Trends: Government Measures: Government Subsidy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s United States – Table US.Google.GT: Google Search Trends: by Categories.

The number of times during the month someone searched the name of a State of Iowa Office with a Google My Business profile using Google Search or while on Google Maps.

The reverse phone number lookup market is experiencing robust growth, driven by increasing concerns over online privacy and safety, the rise of scams and harassment via unknown numbers, and the need for efficient background checks. The market's size in 2025 is estimated at $1.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by several factors. The increasing adoption of smartphones and readily available mobile apps offering this service contributes significantly. Furthermore, businesses across various sectors, from law enforcement and debt collection agencies to background check companies and private investigators, rely on reverse phone lookup services for verifying identities and locating individuals. This widespread adoption across both personal and enterprise applications boosts market expansion. The market segmentation, comprised of cloud-based and on-premise solutions, alongside personal and enterprise applications, indicates diverse user needs and preferences, further fueling the market's growth potential. However, market growth is not without challenges. Data privacy concerns and regulations, such as GDPR and CCPA, pose significant restraints. The potential for inaccuracies in the data provided by some services and the emergence of sophisticated caller ID spoofing techniques also present challenges. Despite these restraints, the overall trend indicates a continuously expanding market, with ongoing technological advancements likely to enhance accuracy and user experience, driving further growth in the coming years. The projected market size in 2033 is estimated to surpass $5 billion, emphasizing the substantial growth potential and market attractiveness. Continued innovation and adaptation to evolving regulatory landscapes will be crucial for sustained success in this dynamic sector.

Google Search Trends: Online Training: Udemy data was reported at 0.000 Score in 14 May 2025. This stayed constant from the previous number of 0.000 Score for 13 May 2025. Google Search Trends: Online Training: Udemy data is updated daily, averaging 0.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 24 Dec 2024 and a record low of 0.000 Score in 14 May 2025. Google Search Trends: Online Training: Udemy data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s Laos – Table LA.Google.GT: Google Search Trends: by Categories.

Google Search Trends: Online Movie: Netflix data was reported at 39.000 Score in 14 May 2025. This records a decrease from the previous number of 42.000 Score for 13 May 2025. Google Search Trends: Online Movie: Netflix data is updated daily, averaging 53.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 100.000 Score in 16 Nov 2024 and a record low of 0.000 Score in 14 Feb 2023. Google Search Trends: Online Movie: Netflix data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s France – Table FR.Google.GT: Google Search Trends: by Categories.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

In 2023/24 the Metropolitan Police of London and the City of London police performed 139,482 stop and searches, a decrease when compared with the previous year. Before 2018/19, the use of this particular police power was declining, with stop and searches falling from 365,589 in 2012/13 to 135,850 in 2017/18.

As of May 2025, Amazon was the biggest internet company worldwide with a market cap of over two trillion U.S. dollars Second-ranked Alphabet had a market capitalization of 1.88 trillion U.S. dollars. The end of the 1990s in the United States saw the rise of a great number of internet companies, also called online companies or a variety of the name “dot com,” where the “.com” domain is derived from the word commercial. At the time, such startups were merely riding the wave of early internet business, but had little capital and perhaps one good idea. Few companies have survived the burst of the dot com bubble and even fewer have managed to become internationally successful. A few notable exceptions are American companies such as Google (founded in 1998), Amazon (founded in 1994) or eBay Inc. (founded in 1995), and the Chinese online giant Alibaba (founded in 1998), which have come to be some of the largest internet companies in the world. One of the largest internet companies worldwide is currently Alphabet, the parent company of Google, with a market capitalization of 1.88 trillion U.S. dollars as of May 2025. Having started as a PhD project at Stanford University, the Google project slowly gained traction and is now the number one search engine in the world, with a market share of 86 percent on the search engine market. Due to a number of high profile acquisitions, Google has expanded its portfolio beyond search, to include the video content sharing site YouTube, the digital app platform Google Play Store, the webmail service Gmail and the web browser Google Chrome, to only name a few. In October 2015, Google reorganized itself into a newly created parent company, the multinational conglomerate Alphabet Inc. The biggest internet companies in terms of their workforce are currently Amazon, Alphabet and Meta.

Ecommerce Search Platform Market size was valued at USD 2 Billion in 2023 and is projected to reach USD 2.5 Billion by 2031, growing at a CAGR of 20% during the forecasted period 2024 to 2031.

Global Ecommerce Search Platform Market Drivers

The market drivers for the Ecommerce Search Platform Market can be influenced by various factors. These may include:

• Increased Online Shopping: The rapid growth of e-commerce and the increasing number of online shoppers have created a demand for efficient search platforms that help users find products quickly and easily. • Technological Advancements: Innovations in AI and machine learning are enhancing the capabilities of e-commerce search platforms, enabling more personalized and accurate search results, which improves the overall user experience.

Global Ecommerce Search Platform Market Restraints

Several factors can act as restraints or challenges for the Ecommerce Search Platform Market. These may include:

• High Implementation Costs: Deploying and maintaining advanced e-commerce search platforms can be expensive, particularly for small and medium-sized enterprises (SMEs). This includes costs associated with software licenses, integration, customization, and ongoing maintenance. • Complex Integration Processes: Integrating e-commerce search platforms with existing systems, such as inventory management, customer relationship management (CRM), and enterprise resource planning (ERP) systems, can be complex and time-consuming. This can deter businesses from adopting such platforms.

Patent Search Software Market size was valued at USD 907.61 Million in 2023 and is projected to reach USD 2,282.62 Million by 2031, growing at a CAGR of 12.32% from 2024 to 2031. Global Patent Search Software Market Overview The Patent Search Software Market is experiencing significant growth, fueled by the ever-increasing volume of patent filings globally. Companies are constantly innovating, and effective patent search has become critical for navigating the complex intellectual property landscape. This software empowers businesses to make informed decisions regarding research and development (R&D), competitive analysis, freedom-to-operate assessments, and patent portfolio management. The market is driven by several key factors. Firstly, the rising number of patent applications worldwide necessitates robust search tools. Patent offices are witnessing a surge in filings, particularly in technology-driven sectors like healthcare and artificial intelligence.

This vast amount of data makes manual searching impractical, and patent search software offers a time-saving and efficient solution. Secondly, the growing emphasis on intellectual property (IP) protection is propelling market growth. Companies recognize the value of their inventions and are increasingly seeking patents to safeguard their competitive edge. Patent search software empowers them to identify potential conflicts with existing patents, ensuring they avoid infringement risks and optimize their patenting strategies. Advancements in artificial intelligence (AI) and machine learning (ML) are adding new dimensions to patent search capabilities. These technologies enable software to analyze vast datasets of patent information with greater accuracy and efficiency.

They can identify relevant patents based on keywords, classifications, and even semantic relationships, leading to more comprehensive and insightful searches. This trend is expected to further accelerate market growth as businesses seek advanced tools to gain a deeper understanding of the competitive landscape. The Patent Search Software Market is poised for continued expansion. The increasing adoption of cloud-based solutions, offering scalability and accessibility, is another factor contributing to market growth. Additionally, the growing focus on international patent filing necessitates software that can navigate the complexities of multiple patent offices and legal jurisdictions. As companies strive for global market reach, the demand for robust and versatile patent search tools is expected to remain high.

Malawi Internet Usage: Search Engine Market Share: Desktop: Shenma data was reported at 0.000 % in 27 Jan 2025. This stayed constant from the previous number of 0.000 % for 26 Jan 2025. Malawi Internet Usage: Search Engine Market Share: Desktop: Shenma data is updated daily, averaging 0.000 % from Jan 2025 (Median) to 27 Jan 2025, with 5 observations. The data reached an all-time high of 0.060 % in 23 Jan 2025 and a record low of 0.000 % in 27 Jan 2025. Malawi Internet Usage: Search Engine Market Share: Desktop: Shenma data remains active status in CEIC and is reported by Statcounter Global Stats. The data is categorized under Global Database’s Malawi – Table MW.SC.IU: Internet Usage: Search Engine Market Share.

The year-over-year monthly change in the number of online hotel searches in the United States increased by 47 percent in October 2023 when compared to 2019. Since January 2020, the monthly change in the number of online hotel searches in the U.S. peaked in August 2023.

This API uses our company's unified registration number to query the basic information of all subsidiary companies under our company.

Using Google Image Search, we conducted searches for “[Plant Latin name] bee” using the list of native Michigan plants that were used in our group’s common garden studies (Table 1) (Fiedler 2006; Fiedler & Landis 2007a; Tuell et al. 2008). Images were evaluated sequentially, in the order they appeared in the search results. Images were evaluated by the following criteria: 1) each search procedure received one tally for each image containing the correct plant species, shown in bloom, and in sufficient detail such that target insect taxa could be reliably identified. 2) in the set of images where the previous condition was met, the number of images where Apis, non-Apis bees, and syrphid flies were visible were tallied and recorded. For each search procedure, photos were sequentially evaluated until 30 images meeting criterion 1 were evaluated, or until 200 images were examined. The number of images evaluated for each search was recorded. Duplicate images and close up shots of previously counted images were excluded from evaluation and not counted towards the total images searched.

################################ variables and descriptions##Plant- plant species on which observations were made##BloomPeriod - bloom period of plant species, as classified by Tuell et al 2008##BloomOrder- As BloomPeriod, but categorized as A, B, C, to enable alphabetical# sorting for creating figures##ImagesSearched - Total number of search results evaluated in order to meet criteria# (see methods for criteria for evaluating search results)##CriteriaMet- total number of images meeting criteria that were evaluated, before# reaching 200 search results##Totinsects- total number of images meeting criteria where 'bee-like' insects were visible##GoogleApis- total number of images meeting criteria where Apis bees were visible##GoogleNonApis - total number of images meeting criteria where non-Apis bees were visible##GoogleBees- total number of images meeting critertia where any type of bee was visible# (GoogleApis+GoogleNonApis)##GoogleSyrphids - total number of images meeting criteria where syrphid flies were visible##JuliannaNonApis- Non-apis bee counts by plant from Tuell et al 2008##JuliannaRichness- Non-apis bee counts by plant from Tuell##JuliannaApis - Apis bee counts by plant from Tuell##JuliannaBees -total bees observed by Tuell (JuliannaApis+JuliannaNonApis)##AnnaSyrphid - syrphid counts from Fielder 2006 thesis by plant