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similarweb.com is ranked #1119 in IN with 18.51M Traffic. Categories: Information Technology, Online Services. Learn more about website traffic, market share, and more!

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

The global website speed test market size was valued at USD 363.9 million in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 15.3% from 2023 to 2032. The growth of the market is attributed to the increasing adoption of online platforms and the need for businesses to optimize their websites for better user experience. Key drivers of the website speed test market include the growing demand for mobile web browsing, the proliferation of content-heavy websites, and the increasing use of personalized content. Additionally, the increasing adoption of cloud-based solutions and the growing awareness of the importance of website performance are expected to drive the growth of the market over the forecast period.

reddit.com is ranked #5 in US with 4.66B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

youtube.com is ranked #1 in KR with 47.12B Traffic. Categories: Newspapers, Online Services. Learn more about website traffic, market share, and more!

The website down checker market is experiencing robust growth, driven by the increasing reliance on online businesses and the critical need for continuous website uptime. The market, estimated at $250 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the expanding e-commerce sector necessitates constant website accessibility to avoid revenue loss and damage to brand reputation. Secondly, the rise of cloud-based services and applications has heightened the demand for reliable uptime monitoring tools. Thirdly, the increasing sophistication of cyberattacks necessitates proactive monitoring to minimize downtime caused by malicious activity. Finally, the growing adoption of diverse website down checkers across various segments, including personal and enterprise use, and across deployment types such as cloud-based and on-premise solutions, contributes significantly to market expansion. The market segmentation reveals a strong preference for cloud-based solutions due to their scalability, cost-effectiveness, and ease of use. The enterprise segment holds a larger market share compared to the personal segment, reflecting the higher reliance on websites for business operations. Geographical distribution shows North America and Europe currently dominating the market, with significant growth potential in Asia Pacific regions fueled by rapid digitalization and expanding internet penetration. However, factors such as the complexity of integrating these checkers into existing IT infrastructure and the availability of free, basic alternatives pose challenges to market expansion. Ongoing technological advancements, however, are expected to mitigate these restraints. The continuous development of more sophisticated monitoring capabilities, including advanced analytics and predictive capabilities, is poised to drive further market expansion in the forecast period.

Many e-shops have started to mark-up product data within their HTML pages using the schema.org vocabulary. The Web Data Commons project regularly extracts such data from the Common Crawl, a large public web crawl. The Web Data Commons Training and Test Sets for Large-Scale Product Matching contain product offers from different e-shops in the form of binary product pairs (with corresponding label “match” or “no match”) for four product categories, computers, cameras, watches and shoes. In order to support the evaluation of machine learning-based matching methods, the data is split into training, validation and test sets. For each product category, we provide training sets in four different sizes (2.000-70.000 pairs). Furthermore there are sets of ids for each training set for a possible validation split (stratified random draw) available. The test set for each product category consists of 1.100 product pairs. The labels of the test sets were manually checked while those of the training sets were derived using shared product identifiers from the Web weak supervision. The data stems from the WDC Product Data Corpus for Large-Scale Product Matching - Version 2.0 which consists of 26 million product offers originating from 79 thousand websites. For more information and download links for the corpus itself, please follow the links below.

Road Test Locations for all DMV Road Test Types mandated by NYS Vehicle and Traffic Law.

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

A dataset of COVID-19 testing sites. A dataset of COVID-19 testing sites. If looking for a test, please use the Testing Sites locator app. You will be asked for identification and will also be asked for health insurance information. Identification will be required to receive a test. If you don’t have health insurance, you may still be able to receive a test by paying out-of-pocket. Some sites may also: - Limit testing to people who meet certain criteria. - Require an appointment. - Require a referral from your doctor. Check a location’s specific details on the map. Then, call or visit the provider’s website before going for a test.

In 2023, the German website test.de had recorded ******* paid views. This was the highest figure since 2009. Test.de is the website of the consumer organization Stiftung Warentest, which tests and compares goods and services.

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

In many parts of the United States and around the globe, the instrumental earthquake record is insufficient to characterize seismic hazard or constrain potential ground motion intensities from individual sources. This lack of data is particularly acute for the Cascadia Subduction Zone (CSZ) of the U.S. Pacific Northwest, where paleoseismic evidence suggests a long history of large megathrust events. While evidence for pre-historic CSZ earthquakes has been discovered onshore and offshore Cascadia, the identification and dating of paleoliquefaction from pre-historic earthquakes offers the best potential for placing quantitative constraints on shaking intensities during past CSZ events. For this dataset, seven Cone Penetration Test (CPT) profiles were collected near five previously dated and published paleoliquefaction sites to enable inverse analysis of paleoshaking intensities during CSZ and Seattle Fault earthquakes. The "CPT Header" file provides the locations, dates, and other basic information for each CPT profile included in this data release. Individual CPT profiles are provided in the "CPT_Profile_Database.zip" directory and are structured as four column .csv files with depth (meters), tip resistance (MPa), sleeve resistance (kPa), and inclination angle of the cone tip from vertical (degrees). The attached metadata document describes the data included in both sets of files.

Note: N = native forest, P = plantation forest, EH = host reduction treatment, EC = control, extra = whether the caterpillar was collected during extra sampling, B = before host-reduction treatment (i.e. at time t), A = after host-reduction treatment (i.e. at time t+1). (CSV)

Scanned Well Potential Test Reports Online Query - You can now request these same well files, well logs, and well data as a free download through the File Request System ( https://www.data.bsee.gov/Other/FileRequestSystem/Default.aspx ). The Disc Media Store will be removed at some point in the future.

The Check-In Dataset is the second public-use dataset in the Dunham's Data series, a unique data collection created by Kate Elswit (Royal Central School of Speech and Drama, University of London) and Harmony Bench (The Ohio State University) to explore questions and problems that make the analysis and visualization of data meaningful for dance history through the case study of choreographer Katherine Dunham. The Check-In Dataset accounts for the comings and goings of Dunham's nearly 200 dancers, drummers, and singers and discerns who among them were working in the studio and theatre together over the years from 1937 to 1962. As with the Everyday Itinerary Dataset, the first public-use dataset from Dunham's Data, data on check-ins come from scattered sources. Due to information available, it has a greater level of ambiguity as many dates are approximated in order to achieve accurate chronological sequence. By showing who shared time and space together, the Check-In Dataset can be used to trace potential lines of transmission of embodied knowledge within and beyond the Dunham Company. Dunham's Data: Digital Methods for Dance Historical Inquiry is funded by the United Kingdom Arts and Humanities Research Council (AHRC AH/R012989/1, 2018-2022) and is part of a larger suite of ongoing digital collaborations by Bench and Elswit, Movement on the Move. The Dunham's Data team also includes digital humanities postdoctoral research assistant Antonio Jiménez-Mavillard and dance history postdoctoral research assistants Takiyah Nur Amin and Tia-Monique Uzor. For more information about Dunham's Data, please see the Dunham's Data website. Also, visit the Dunham's Data research blog to view the interactive visualizations based on the Dunham's Data.

Testing web APIs automatically requires generating input data values such as addressess, coordinates or country codes. Generating meaningful values for these types of parameters randomly is rarely feasible, which means a major obstacle for current test case generation approaches. In this paper, we present ARTE, the first semantic-based approach for the Automated generation of Realistic TEst inputs for web APIs. Specifically, ARTE leverages the specification of the API under test to extract semantically related values for every parameter by applying knowledge extraction techniques. Our approach has been integrated into RESTest, a state-of-the-art tool for API testing, achieving an unprecedented level of automation which allows to generate up to 100\% more valid API calls than existing fuzzing techniques (30\% on average). Evaluation results on a set of 26 real-world APIs show that ARTE can generate realistic inputs for 7 out of every 10 parameters, outperforming the results obtained by related approaches.

The Website Down Checker market has become an increasingly vital sector within the digital landscape, as businesses of all sizes seek to ensure their online presence remains accessible and reliable. These tools help quickly identify when a website is not functioning, providing immediate alerts that can save companie

The 311 website allows residents to submit service requests or check the status of existing requests online. The percentage of 311 website uptime, the amount of time the site was available, and the target uptime for each week are available by mousing over columns. The target availability for this site is 99.5%.