The Performance and Operational Web-Enabled Reports (POWER) system is a state-of-the-art data warehouse containing data on Veterans Health Administration (VHA) performance metrics that are obtained daily from the individual Veterans Health Information Systems and Technology Architecture (VistA) systems.The POWER system was developed to measure the key performance indicators across VHA facilities and is helping to improve VHA's Medical Care Collections Fund (MCCF) revenue operational performance by providing accurate, reliable, and up-to-date performance measure information. POWER leverages a data warehouse to maintain data used in VHA performance measure calculations. The site provides Web-based analytical reporting capabilities, allowing users to view data by dimensions, such as, National, Consolidated Patient Account Center (CPAC), Veterans Integrated Service Network (VISN), or Station locations and by month. The data can also be displayed in tables, graphs and spreadsheets. It should be noted that POWER is not an accounting system; rather, it is a strategic and operational performance reporting system.The POWER system supports VHA's efforts to improve its revenue business operations by providing accurate and reliable performance information on the following metrics: Collections, Gross Days Revenue Outstanding (GDRO), Percentage of Accounts Receivable (AR) Greater than 90 Days, Days to Bill, Total Billings, Percentage of Collections to Billings, and Cost to Collect. POWER is VHA's revenue performance metric dashboard monitoring system that tracks MCCF performance by National, CPAC, VISN and Station.

Introduction

Web Design Statistics: ​As of 2024, the World Wide Web encompasses approximately 1.1 billion websites, with only about 17%—equating to roughly 200 million—actively maintained and visited. This vast digital landscape continues to expand, with an estimated 252,000 new websites emerging daily, underscoring the dynamic nature of the internet. ​
Sixth City Marketing.

In this extensive online environment, web design plays a pivotal role in influencing user engagement and business success. Notably, 94% of first impressions are design-related, highlighting the critical importance of a website's visual appeal in shaping user perceptions. Furthermore, 75% of users admit to making judgments about a company's credibility based on its website design, emphasizing the direct correlation between design quality and trustworthiness. ​

User experience is equally significant, as 88% of online consumers are less likely to return to a site after a bad experience, and 90% of users have stopped using a website due to poor design. Additionally, 73.1% of users leave a website if it is non-responsive, underlining the necessity for mobile-friendly designs.

These statistics collectively underscore the critical importance of effective web design in today's digital landscape. A well-designed website not only enhances user satisfaction but also significantly contributes to a company's credibility and financial performance.

As of June 2020, the COVID-19 pandemic has impacted the speed of mobile and fixed broadband internet networks worldwide. In Trinidad and Tobago, mobile internet speed was up 106 percent compared to the benchmark week ending March 2, 2020. In comparison, Kenya's fixed internet speed declined by 21 percent. In the United States, fixed and mobile internet speed increased by four and six percent respectively.

The website availability metrics below are derived from an automated monitor that sends a request every two minutes to each website. The website is considered unavailable if the response to any request takes longer than a pre-defined wait time. The monitors run continuously and are not normally disabled during scheduled maintenance or downtime, so the reported metrics incorporate both planned and unplanned downtime.

This dataset is about book subjects and is filtered where the books is Web performance in action : building faster web pages. It has 10 columns such as book subject, earliest publication date, latest publication date, average publication date, and number of authors. The data is ordered by earliest publication date (descending).

Fiber Net is a pioneer in business internet solutions, established in 1994. The company takes pride in its commitment to providing reliable, high-performance, and secure internet services to its customers. Fiber Net's extensive range of services includes colocation, cybersecurity, managed IT, and internet services, catering to businesses of all sizes and industries.

Fiber Net's data centers are designed to offer scalable and reliable solutions, with 247 monitoring and support. The company's expertise lies in its ability to empower businesses through cutting-edge solutions, ensuring seamless operations and maximum productivity. With a strong reputation for reliability and dedication to customer success, Fiber Net is an ideal partner for businesses seeking to thrive in the digital landscape.

Snapshot img

Web Performance Market Size 2024-2028

The web performance market size is forecast to increase by USD 2.8 billion, at a CAGR of 8.12% between 2023 and 2028. The market is experiencing significant growth, driven by the increasing use of advanced web technologies and the expanding Internet user base. Key trends include the demand for mobile web performance solutions due to the rise in mobile internet usage, as well as the integration of security services such as Web Application Firewall as a Service (WAFaaS) and multi-factor authentication to enhance security. Sectors like media and entertainment, healthcare, transportation, logistics and manufacturing are major contributors to the market's growth, as they rely heavily on web applications for business operations. However, high deployment costs and the increasing number of cyber attacks pose challenges to market growth. The need for faster web performance and security measures is becoming crucial for businesses to maintain a competitive edge and provide a seamless user experience.

Market Analysis

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The market is experiencing significant growth due to the increasing reliance on the Internet for business operations and consumer engagement. The e-commerce industry, in particular, is driving the demand for improved web performance as online sales continue to stream. Content Delivery Networks (CDNs) play a crucial role in delivering content quickly and efficiently, especially for large media files such as images. IT and telecom sectors are also investing heavily in web performance to enhance user experience. E-commerce sites, media and entertainment, healthcare, logistics and transportation, and the SME sector are some of the key industries leveraging web performance technologies.

Moreover, mobile devices have further complicated the digital landscape, necessitating optimization for various screen sizes and mobile-specific protocols like Transmission Control Protocol (TCP) and Hypertext Transfer Protocol (HTTP). Search engine optimization, user experience, and privacy laws are critical factors influencing web performance. Tools like SolarWinds and AppDynamics help monitor and optimize web performance, while Hypertext Markup Language (HTML) and HTTP/2 enable faster content delivery. Mobile penetration continues to rise, making it essential for businesses to prioritize web performance to cater to the growing mobile user base.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On premise
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  APAC

    China


  Middle East and Africa



  South America

By Deployment Insights

The on premise segment is estimated to witness significant growth during the forecast period. The market witnessed significant growth in 2023, with on-premises solutions holding the largest market share. This model necessitates substantial investments for product purchases, installation, maintenance, and upgrades. Additionally, organizations must hire and train an in-house IT workforce to support users, which can be costly for small and medium-sized enterprises (SMEs). Despite the expense, large enterprises, primarily those dealing with sensitive data, prefer the on-premises model due to its enhanced security features. This model ensures end-to-end quality control and eliminates third-party involvement, making it a popular choice for organizations concerned with data security. In the dynamic digital landscape of the e-commerce industry, web performance solutions, such as those offered by SEMrush and application delivery controllers, play a crucial role in ensuring optimal website performance on both desktop and mobile devices.

Security breaches can lead to significant financial losses and reputational damage, making the need for reliable web performance solutions more pressing than ever.

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The on-premise segment was valued at USD 3.29 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 39% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

In the dynamic digital landscape of North America, particularly in the US, the e-commerce industry is experiencing significant growth. This expansion is driven by several factors, including the increasing number of cybersecurity breaches and the transition from traditional

This statistic shows the energy performance of Amazon Web Services according to Greenpeace's Clean Energy Index in 2016. That year, Amazon Web Services was rated with a Clean Energy Index of 17 percent.

This data set provides fixed broadband network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. For more information please see the Ookla Github repository or the Registry of Open Data on AWS. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access. AURIN has generated a subset corresponding to the intersection with the extent of Australia. It was then reprojected from EPSG 4326 (WGS84) to 4283 (GDA94).

The statistic shows the average internet connection speed in the United States from 2007 to 2017. In the first quarter of 2017, the average internet connection speed was 18.75 Mbps.

The chart provides an insightful analysis of the estimated sales amounts for Internet stores across various platforms. Custom Cart stands out, generating a significant portion of sales with an estimated amount of $4.87B, which is 67.33% of the total sales in this category. Following closely, WooCommerce accounts for $1.06B in sales, making up 14.67% of the total. Shopify also shows notable performance, contributing $645.94M to the total sales, representing 8.94%. This data highlights the sales dynamics and the varying impact of each platform on the Internet market.

The performance-based internet advertising expenditure in Japan amounted to approximately 2.35 trillion Japanese yen in 2023. The value more than quadrupled since 2014.

The City's Internet site allows residents to access City services online, learn more about the City of Chicago, and find other pertinent information. The percentage of the City’s Internet 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%.

The purpose of this study was to explore empirical evidence to support the performance measurement framework identified by the American Prosecutors Research Institute's (APRI) Prosecution Study for the 21st Century. The framework included promoting the fair, impartial, and expeditious pursuit of justice and ensuring public safety. Two prosecutors' offices participated in the study. One was a traditional office, and the other was a more community-oriented office. Each site submitted monthly data on the identified performance measures for analysis. APRI also designed and administered a public safety survey to assess a number of factors related to the performance of prosecutors' offices, but for which data could not be provided by the offices. The public safety surveys were administered by phone using random digit dialing (RDD). Part 1, Site 1 Administrative Data, contains 15 months of data, and Part 2, Site 2 Administrative Data, contain 6 months of data on the identified performance measures for analysis from each site. Part 3, Public Safety Survey Data, contains variables that provide some basic demographic data about the respondents and several variables in response to Likert-style questions measuring attitudes and opinions on six factors. These include the seriousness of local crime, safety of the environment, organizational behavior of the prosecutor's office, participation in the community, community education, and task performance.

This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]DAISY[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]DAISY[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).

The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (http://rampanalytics.org), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.

Methods

RAMP Data Documentation – January 1, 2017 through August 18, 2018

Data Collection

RAMP data were downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data from January 1, 2017 through August 18, 2018 were downloaded in one dataset per participating IR. The following fields were downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.

Following data processing describe below, on ingest into RAMP an additional field, citableContent, is added to the page level data.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, data are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the data which records whether each URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

Processed data are then saved in a series of Elasticsearch indices. From January 1, 2017, through August 18, 2018, RAMP stored data in one index per participating IR.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository content, CCD represent click activity on IR content that may correspond to research use.

CCD information is summary data calculated on the fly within the RAMP web application. As noted above, data provided by GSC include whether and how many times a URL was clicked by users. Within RAMP, a "click" is counted as a potential download, so a CCD is calculated as the sum of clicks on pages/URLs that are determined to point to citable content (as defined above).

For any specified date range, the steps to calculate CCD are:

Filter data to only include rows where "citableContent" is set to "Yes."
Sum the value of the "clicks" field on these rows.

Output to CSV

Published RAMP data are exported from the production Elasticsearch instance and converted to CSV format. The CSV data consist of one "row" for each page or URL from a specific IR which appeared in search result pages (SERP) within Google properties as described above.

The data in these CSV files include the following fields:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
country: The country from which the corresponding search originated.
device: The device used for the search.
date: The date of the search.
citableContent: Whether or not the URL points to a content file (ending with pdf, csv, etc.) rather than HTML wrapper pages. Possible values are Yes or No.
index: The Elasticsearch index corresponding to page click data for a single IR.
repository_id: This is a human readable alias for the index and identifies the participating repository corresponding to each row. As RAMP has undergone platform and version migrations over time, index names as defined for the index field have not remained consistent. That is, a single participating repository may have multiple corresponding Elasticsearch index names over time. The repository_id is a canonical identifier that has been added to the data to provide an identifier that can be used to reference a single participating repository across all datasets. Filtering and aggregation for individual repositories or groups of repositories should be done using this field.

Filenames for files containing these data follow the format 2018-01_RAMP_all.csv. Using this example, the file 2018-01_RAMP_all.csv contains all data for all RAMP participating IR for the month of January, 2018.

Data Collection from August 19, 2018 Onward

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data processing describe below, on ingest into RAMP a additional field, citableContent, is added to the page level data.

The second set includes similar information, but instead of being aggregated at the page level, the data are grouped based on the country from which the user submitted the corresponding search, and the type of device used. The following fields are downloaded for combination of country and device, with one row per country/device combination:

country: The country from which the corresponding search originated.
device: The device used for the search.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Note that no personally identifiable information is downloaded by RAMP. Google does not make such information available.

More information about click-through rates, impressions, and position is available from Google's Search Console API documentation: https://developers.google.com/webmaster-tools/search-console-api-original/v3/searchanalytics/query and https://support.google.com/webmasters/answer/7042828?hl=en

Data Processing

Upon download from GSC, the page level data described above are processed to identify URLs that point to citable content. Citable content is defined within RAMP as any URL which points to any type of non-HTML content file (PDF, CSV, etc.). As part of the daily download of page level statistics from Google Search Console (GSC), URLs are analyzed to determine whether they point to HTML pages or actual content files. URLs that point to content files are flagged as "citable content." In addition to the fields downloaded from GSC described above, following this brief analysis one more field, citableContent, is added to the page level data which records whether each page/URL in the GSC data points to citable content. Possible values for the citableContent field are "Yes" and "No."

The data aggregated by the search country of origin and device type do not include URLs. No additional processing is done on these data. Harvested data are passed directly into Elasticsearch.

Processed data are then saved in a series of Elasticsearch indices. Currently, RAMP stores data in two indices per participating IR. One index includes the page level data, the second index includes the country of origin and device type data.

About Citable Content Downloads

Data visualizations and aggregations in RAMP dashboards present information about citable content downloads, or CCD. As a measure of use of institutional repository

Data Collection

RAMP data are downloaded for participating IR from Google Search Console (GSC) via the Search Console API. The data consist of aggregated information about IR pages which appeared in search result pages (SERP) within Google properties (including web search and Google Scholar).

Data are downloaded in two sets per participating IR. The first set includes page level statistics about URLs pointing to IR pages and content files. The following fields are downloaded for each URL, with one row per URL:

url: This is returned as a 'page' by the GSC API, and is the URL of the page which was included in an SERP for a Google property.
impressions: The number of times the URL appears within the SERP.
clicks: The number of clicks on a URL which took users to a page outside of the SERP.
clickThrough: Calculated as the number of clicks divided by the number of impressions.
position: The position of the URL within the SERP.
date: The date of the search.

Following data process...

This data set provides fixed broadband network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy. For more information please see the Ookla Github repository or the Registry of Open Data on AWS. Ookla licenses data to NGOs and educational institutions to fulfill its mission: to help make the internet better, faster and more accessible for everyone. Ookla hopes to further this mission by distributing the data to make it easier for individuals and organizations to use it for the purposes of bridging the social and economic gaps between those with and without modern Internet access. AURIN has generated a subset corresponding to the intersection with the extent of the 2016 Greater Capital City Statistical Areas (GCCSA) boundaries from the ABS Australian Statistical Geography Standard (ASGS). It was then reprojected from EPSG 4326 (WGS84) to 4283 (GDA94). Additional columns have been added corresponding to the matching boundaries of the 2016 ABS Statistical Area Levels 2, 3 and 4, and the 2020 Local Government Areas. These were spatially joined using the centroid of each polygon, and therefore, should only be used as an approximation. Furthermore, grid cells residing outside of these boundaries, such as offshore or over rivers, are assigned a null value in these columns.

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As of June 2024, Qatar had the fastest average mobile internet connection worldwide, nearly 335 Mbps. The United Arab Emirates (UAE) followed, registering average median speed above 323 Mbps. Fixed-connection speeds around the world When it comes to fixed broadband connections, Singapore tops the list of countries by average connection speed. Internet users in Singapore achieve an average fixed broadband connection speed of 242.01 Mbps, slightly faster than the 222.49 Mbps achieved in Chile, the second-placed country on the speed rankings. 5G and 6G – the future of mobile broadband In countries where it is in use, 5G is already bringing faster mobile internet connection speeds than ever before. In Saudi Arabia for example, the average 4G connection speed sits at 28.9 Mbps, and this speed jumps to 414.2 Mbps on a 5G connection. Now that 5G is commercially available, researchers have already turned their attention to 6G. Operating at a higher spectrum band, 6G will allow connections several times faster than 5G. User experienced data rates of 5G sit at 100 Mbps, and this speed is expected to climb to 1,000 Mbps on 6G connections. 6G is expected to not only provide faster speeds, but also enable more devices to connect to a network without causing congestion as it has a connection density ten times greater than that of 5G.