Dataset

This dataset was created by Merve Afranur ARTAR

Contents

This data about nola.gov provides a window into how people are interacting with the the City of New Orleans online. The data comes from a unified Google Analytics account for New Orleans. We do not track individuals and we anonymize the IP addresses of all visitors.

The Big Data Analysis Software market is experiencing robust growth, driven by the increasing volume of data generated across various sectors and the rising need for actionable insights. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The widespread adoption of cloud-based solutions offers scalability, cost-effectiveness, and accessibility, accelerating market penetration. Furthermore, the growing demand for real-time analytics across industries like banking, manufacturing, and government is a major driver. Specific trends include the increasing integration of AI and machine learning into analytics platforms, enhancing predictive capabilities and automating processes. However, challenges remain, such as data security concerns, the complexity of implementing and managing big data solutions, and the skills gap in data science expertise. These factors represent potential restraints on market growth, though ongoing technological advancements and increased investment in data literacy initiatives are mitigating these issues. The market is segmented by deployment type (cloud-based and on-premises) and application (banking, manufacturing, consultancy, government, and others), with cloud-based solutions dominating due to their inherent advantages. The competitive landscape is highly dynamic, featuring both established technology giants like Google, Amazon, and IBM, alongside specialized software providers such as Rohde & Schwarz and Qlucore. The diversity of players indicates a wide range of solutions catering to diverse needs and market segments. Regional growth is expected to be diverse, with North America and Europe maintaining substantial market shares due to early adoption and advanced technological infrastructure. However, rapidly developing economies in Asia-Pacific and the Middle East & Africa are poised for significant growth, presenting lucrative opportunities for market expansion. The forecast period (2025-2033) anticipates continued market expansion, driven by technological innovations, increasing data volumes, and growing adoption across various industries and geographies. The market's long-term prospects remain positive, indicating a significant return on investment for businesses involved in its development and implementation.

Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

The data set provided (traffic.csv) contains web traffic data ("events") from a few different pages ("links") over 7 days including various categorical dimensions about the geographic origin of that traffic as well as a page's content: isrc.

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.

Snapshot img

Web Analytics Market Size 2025-2029

The web analytics market size is forecast to increase by USD 3.63 billion, at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the rising preference for online shopping and the increasing adoption of cloud-based solutions. The shift towards e-commerce is fueling the demand for advanced web analytics tools that enable businesses to gain insights into customer behavior and optimize their digital strategies. Furthermore, cloud deployment models offer flexibility, scalability, and cost savings, making them an attractive option for businesses of all sizes. However, the market also faces challenges associated with compliance to data privacy and regulations. With the increasing amount of data being generated and collected, ensuring data security and privacy is becoming a major concern for businesses.
Regulatory compliance, such as GDPR and CCPA, adds complexity to the implementation and management of web analytics solutions. Companies must navigate these challenges effectively to maintain customer trust and avoid potential legal issues. To capitalize on market opportunities and address these challenges, businesses should invest in robust web analytics solutions that prioritize data security and privacy while providing actionable insights to inform strategic decision-making and enhance customer experiences.

What will be the Size of the Web Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities unfolding across various sectors. Entities such as reporting dashboards, schema markup, conversion optimization, session duration, organic traffic, attribution modeling, conversion rate optimization, call to action, content calendar, SEO audits, website performance optimization, link building, page load speed, user behavior tracking, and more, play integral roles in this ever-changing landscape. Data visualization tools like Google Analytics and Adobe Analytics provide valuable insights into user engagement metrics, helping businesses optimize their content strategy, website design, and technical SEO. Goal tracking and keyword research enable marketers to measure the return on investment of their efforts and refine their content marketing and social media marketing strategies.

Mobile optimization, form optimization, and landing page optimization are crucial aspects of website performance optimization, ensuring a seamless user experience across devices and improving customer acquisition cost. Search console and page speed insights offer valuable insights into website traffic analysis and help businesses address technical issues that may impact user behavior. Continuous optimization efforts, such as multivariate testing, data segmentation, and data filtering, allow businesses to fine-tune their customer journey mapping and cohort analysis. Search engine optimization, both on-page and off-page, remains a critical component of digital marketing, with backlink analysis and page authority playing key roles in improving domain authority and organic traffic.

The ongoing integration of user behavior tracking, click-through rate, and bounce rate into marketing strategies enables businesses to gain a deeper understanding of their audience and optimize their customer experience accordingly. As market dynamics continue to evolve, the integration of these tools and techniques into comprehensive digital marketing strategies will remain essential for businesses looking to stay competitive in the digital landscape.

How is this Web Analytics Industry segmented?

The web analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Application

  Social media management
  Targeting and behavioral analysis
  Display advertising optimization
  Multichannel campaign analysis
  Online marketing


Component

  Solutions
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

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By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

In today's digital landscape, web analytics plays a pivotal role in driving business growth and optimizing online performance. Cloud-based deployment of web analytics is a game-changer, enabling on-demand access to computing resources for data analysis. This model streamlines business intelligence processes by collecting, integra

Contains view count data for the top 20 pages each day on the Somerville MA city website dating back to 2020. Data is used in the City's dashboard which can be found at https://www.somervilledata.farm/.

Information about pages on the City's website including their age and their Google Analytics data (everything from "PageViews" and to the right). If the Google Analytics fields are empty, the page hasn't been visited recently at all.

1- The Zieni Dataset (2024): This is a recent, balanced dataset comprising 10,000 websites, with 5,000 phishing and 5,000 legitimate samples. The phishing URLs were sourced from PhishTank and Tranco, while legitimate URLs came from Alexa. Each of the 10,000 instances is characterized by 74 features, with 70 being numerical and 4 binary. These features comprehensively describe various components of a URL, including the domain, path, filename, and parameters.

2- The UCI Phishing Websites Dataset: This dataset contains 11,055 website instances, each labeled as either phishing (1) or legitimate (-1). It provides 30 diverse features that capture address bar characteristics, domain-based attributes, and other HTML and JavaScript elements (e.g., prefix-suffix, google_index, iframe, https_token). The data was aggregated from several reputable sources, including the PhishTank and MillerSmiles archives.

3- The Mendeley Phishing Dataset: This dataset includes 10,000 webpages, evenly split between phishing and legitimate categories. It describes each sample using 48 features. The data was collected in two periods: from January to May 2015 and from May to June 2017.

References [1] R. Zieni, “Zieni dataset for Phishing detection,” vol. 1, 2024. doi: 10.17632/8MCZ8JSGNB.1. [2] R. Mohammad et al., “An assessment of features related to phishing websites using an automated technique,” in International Conference for Internet Technology and Secured Transactions, 2012. [3] C. L. Tan, “Phishing Dataset for Machine Learning: Feature Evaluation,” vol. 1, 2018. doi: 10.17632/H3CGNJ8HFT.1.

This Website Statistics dataset has four resources showing usage of the Lincolnshire Open Data website. Web analytics terms used in each resource are defined in their accompanying Metadata file.

• Website Usage Statistics: This document shows a statistical summary of usage of the Lincolnshire Open Data site for the latest calendar year.

• Website Statistics Summary: This dataset shows a website statistics summary for the Lincolnshire Open Data site for the latest calendar year.

• Webpage Statistics: This dataset shows statistics for individual Webpages on the Lincolnshire Open Data site by calendar year.

• Dataset Statistics: This dataset shows cumulative totals for Datasets on the Lincolnshire Open Data site that have also been published on the national Open Data site Data.Gov.UK - see the Source link.

  Note: Website and Webpage statistics (the first three resources above) show only UK users, and exclude API calls (automated requests for datasets). The Dataset Statistics are confined to users with javascript enabled, which excludes web crawlers and API calls.

These Website Statistics resources are updated annually in January by the Lincolnshire County Council Business Intelligence team. For any enquiries about the information contact opendata@lincolnshire.gov.uk.

The global Enterprise Website Analytics Software market is experiencing robust growth, driven by the increasing need for businesses to understand their online presence and optimize their digital strategies. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the proliferation of mobile devices and diverse digital channels requiring sophisticated analytics, and a growing focus on data-driven decision-making across all departments. Large enterprises are leading the adoption, leveraging these tools for detailed customer journey mapping, performance optimization, and enhanced ROI on marketing investments. However, the market faces challenges such as the complexity of integrating various analytics platforms and the need for specialized expertise to effectively interpret and utilize the vast amounts of data generated. The segment showing the fastest growth is likely cloud-based solutions due to their flexibility and accessibility. We estimate the 2025 market size to be around $15 billion, based on observable growth trends in related software markets and considering the increasing adoption of analytics solutions across various industries. A Compound Annual Growth Rate (CAGR) of 12% is projected for the forecast period (2025-2033), indicating substantial market expansion over the coming years. The competitive landscape is highly dynamic, with both established tech giants (Google, IBM) and specialized analytics providers (Adobe, SEMrush, Mixpanel) vying for market share. The ongoing trend towards mergers and acquisitions further shapes the industry. Companies are continually innovating to offer more comprehensive solutions, incorporating features like artificial intelligence (AI) for predictive analytics, real-time data visualization, and seamless integration with CRM systems. Geographic growth will vary, with North America and Europe expected to maintain significant market share due to high technological adoption rates. However, Asia-Pacific is projected to witness substantial growth driven by increasing digitalization and economic expansion. The market's future trajectory hinges on continuous innovation within analytics capabilities, addressing the challenges of data privacy and security, and fostering greater user-friendliness within these sophisticated platforms.

A RESTful web service for querying data and metadata components from data sets, including instruments, observatories, and inventory. This interface calls the services of the SPDF CDAWeb data browsing system. The Space Physics Data Facility (SPDF) is the archive of non-solar data for the Heliospheric Science Division (HSD) at NASA's Goddard Space Flight Center.

The Web Analytics Market Report is Segmented by Application ( Mobile Analytics, Content Marketing, Social Media Management, and More), Offering (Solutions, and Services), Deployment Model (Cloud-Based, and On-Premises), Organization Size (Large Enterprises, and Small and Medium Enterprises), End-User Vertical (Retail and E-Commerce, Manufacturing, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

The market for website analytics tools is witnessing significant growth, driven by the increasing adoption of digital marketing and the need for businesses to track and measure the effectiveness of their online campaigns. The market is projected to reach a value of XXX million by 2033, exhibiting a CAGR of XX% over the forecast period (2025-2033). Cloud-based deployment models and the growing adoption of website analytics tools by SMEs are contributing to the market's expansion. Key trends shaping the website analytics tool market include the adoption of artificial intelligence (AI) and machine learning (ML) to enhance data analysis capabilities, the growing importance of data privacy and security, and the emergence of real-time analytics. The increasing availability of open-source website analytics tools is also expected to intensify competition in the market. Prominent players in the market include Matomo, Google Analytics, Tiny Analytics, Mixpanel, SimilarWeb, Hotjar, Woopra, Adobe Analytics, Statcounter, Amplitude, Heap, Visual Website Optimizer (VWO), Kissmetrics, Indicative, Chartbeat, and many others. The market is segmented across regions including North America, South America, Europe, Middle East & Africa, and Asia Pacific.

Web Analytics Platform Market Outlook

According to our latest research, the global web analytics platform market size in 2024 is valued at USD 7.21 billion, with robust growth trends driven by the increasing digitalization of businesses and the need for data-driven decision-making. The market is exhibiting a promising CAGR of 15.8% from 2025 to 2033, and is expected to reach USD 26.62 billion by 2033. The primary growth factor fueling this expansion is the escalating demand for actionable insights from online user behavior, which is critical for optimizing marketing strategies and enhancing customer experiences.

One of the key growth drivers for the web analytics platform market is the exponential increase in internet penetration and digital transformation across industries. As organizations shift their operations online, the volume of data generated from web interactions has surged, necessitating advanced analytics solutions to derive meaningful insights. Businesses are leveraging web analytics to monitor website performance, track user engagement, and personalize customer journeys, which in turn boosts conversion rates and revenue generation. The proliferation of e-commerce platforms and the growing importance of omnichannel marketing have further accelerated the adoption of sophisticated analytics tools, enabling enterprises to stay competitive in an increasingly digital ecosystem.

Another significant factor propelling market growth is the integration of artificial intelligence (AI) and machine learning (ML) technologies into web analytics platforms. These advancements empower organizations to automate data collection, enhance predictive analytics, and uncover deep behavioral patterns that were previously inaccessible through traditional analytics methods. The ability to process vast datasets in real-time and generate actionable insights has transformed how businesses approach digital marketing and customer engagement. Moreover, AI-powered analytics platforms are increasingly being used for targeting and behavioral analysis, multichannel campaign optimization, and real-time decision-making, which are vital in todayÂ’s fast-paced digital landscape.

The surge in demand for personalized customer experiences and data privacy compliance is also shaping the future of the web analytics platform market. With consumers expecting tailored interactions and regulatory bodies enforcing stricter data protection laws, businesses are investing in analytics platforms that offer robust privacy features and transparency. This dual focus on personalization and compliance is driving innovation, with vendors developing solutions that provide granular insights while ensuring data security. The growing adoption of cloud-based analytics platforms, which offer scalability, flexibility, and cost-efficiency, is further amplifying market growth, especially among small and medium enterprises (SMEs) seeking to leverage enterprise-grade analytics without significant upfront investments.

From a regional perspective, North America continues to dominate the web analytics platform market, accounting for the largest market share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is attributed to the presence of major technology providers, early adoption of digital marketing strategies, and a mature e-commerce ecosystem. However, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing internet and smartphone penetration, and a burgeoning e-commerce sector. The Middle East & Africa and Latin America are also experiencing steady growth, fueled by rising investments in digital infrastructure and a growing emphasis on data-driven business strategies. As organizations across regions recognize the strategic value of web analytics in achieving business objectives, the market is poised for sustained expansion through 2033.

Component Analysis

The web analytics platform market is segmented by component into software and services, each playing a pivotal role in the adoption and effectiveness of analytics solutions. Software solu

Construction Data Analytics Platform Market Outlook

According to our latest research, the global construction data analytics platform market size reached USD 6.2 billion in 2024, reflecting the rapidly growing adoption of digital solutions in the construction sector. The market is expected to grow at a strong CAGR of 15.7% during the forecast period, reaching USD 22.4 billion by 2033. This robust growth is primarily driven by the increasing need for real-time data-driven decision-making, enhanced project efficiency, and the rising integration of advanced analytics and AI technologies across the construction industry.

The accelerated digital transformation in the construction sector is a pivotal growth factor for the construction data analytics platform market. Construction firms are now prioritizing the deployment of data analytics platforms to streamline operations, improve project management, and gain actionable insights from vast amounts of data generated on-site and off-site. The proliferation of IoT-enabled devices and sensors on construction sites has resulted in a surge of data, necessitating sophisticated analytics solutions to harness this information for predictive maintenance, risk mitigation, and resource optimization. Furthermore, the growing emphasis on reducing project delays, cost overruns, and safety incidents is compelling stakeholders to invest in robust analytics platforms that can deliver real-time monitoring and predictive insights, thus fostering operational excellence.

Another significant driver propelling the construction data analytics platform market is the increasing adoption of cloud-based solutions. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, making them particularly attractive for construction firms of all sizes. Cloud-based analytics platforms enable seamless collaboration among geographically dispersed project teams, facilitate remote monitoring, and ensure that stakeholders have access to up-to-date project data at all times. This shift towards cloud technology is further supported by the integration of AI and machine learning algorithms, which enhance the predictive capabilities of these platforms, automate routine tasks, and deliver deeper insights into project performance, risk factors, and resource allocation.

The heightened focus on regulatory compliance and safety standards in the construction industry is also contributing to the market’s expansion. Governments and industry bodies worldwide are imposing stringent regulations to ensure worker safety and environmental sustainability. Construction data analytics platforms play a crucial role in helping firms adhere to these regulations by providing real-time tracking of safety metrics, automating compliance reporting, and identifying potential hazards before they escalate. The ability to leverage analytics for proactive risk management and compliance monitoring is increasingly seen as a competitive advantage, driving widespread adoption of these platforms across the sector.

Regionally, North America continues to dominate the construction data analytics platform market, supported by the early adoption of digital construction technologies and a mature IT infrastructure. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid urbanization, significant infrastructure investments, and government initiatives promoting digital transformation in construction. Europe also demonstrates steady growth, driven by stringent regulatory requirements and a strong focus on sustainable construction practices. Meanwhile, Latin America and the Middle East & Africa are gradually embracing analytics platforms, primarily in large-scale infrastructure and smart city projects, albeit at a slower pace due to budget constraints and limited digital maturity.

Component Analysis

The construction data analytics platform market is segmented by component into software and services, with software accounting for the largest share in 2024. Software soluti

The Big Data User Behavior Analysis Platform market is experiencing robust growth, driven by the increasing need for businesses to understand user interactions and optimize digital experiences. The market, estimated at $15 billion in 2025, is projected to expand significantly over the next decade, fueled by a Compound Annual Growth Rate (CAGR) of 15%. This growth is propelled by several key factors: the proliferation of digital channels, the rise of personalized marketing strategies, the increasing adoption of cloud-based analytics solutions, and the growing demand for real-time data insights. Key market segments, including e-commerce and website analysis platforms, are witnessing particularly strong growth, as businesses leverage these platforms to improve conversion rates, customer retention, and overall business performance. The competitive landscape is marked by a mix of established players like Google and Adobe, alongside specialized analytics vendors such as Mixpanel and Amplitude. These companies are continuously innovating, incorporating advanced technologies like AI and machine learning to enhance their offerings and cater to evolving business needs. The geographic distribution of the market is diverse, with North America and Europe currently holding the largest market shares. However, rapid growth is anticipated in Asia-Pacific regions like India and China, fueled by increasing internet penetration and digital adoption. While the market faces certain restraints, such as data privacy concerns and the complexity of implementing big data analytics solutions, these challenges are being mitigated by advancements in data security technologies and user-friendly analytics platforms. The ongoing trend towards real-time analytics and predictive modeling will further drive market expansion, empowering businesses to make data-driven decisions with greater speed and accuracy. The forecast period of 2025-2033 promises substantial growth opportunities for both established players and emerging startups in this dynamic sector.

The dataset obtained from web scraping encompasses a diverse set of news articles from prominent sources: Al Jazeera, BBC News Arabic, Fatabyyano, Verify-Sy and matsda2sh. Each article provides unique insights into various topics, ranging from global politics and current affairs to health, culture, and technology. The dataset offers a comprehensive snapshot of contemporary news coverage, allowing for in-depth analysis and exploration of different perspectives. With detailed information on article titles, categories, publication dates, and content, researchers and analysts can gain valuable insights into arabic media trends, public discourse, and societal issues.

The Web Analytics market offers a diverse range of sophisticated solutions tailored to various business needs. Key product categories include: Web Analytics Software: These comprehensive platforms provide robust data collection, insightful analysis, and customizable reporting features for both websites and mobile applications, often incorporating advanced features like A/B testing and heatmaps. Data Management Platforms (DMPs): DMPs are crucial for consolidating data from disparate sources – CRM systems, marketing automation platforms, and web analytics tools – enabling a unified, holistic view of customer interactions and behaviors. Real-time Analytics Platforms: These solutions offer immediate insights into website performance and user behavior, allowing for swift responses to emerging trends and potential issues. They are invaluable for immediate adjustments to campaigns and website functionality. Customer Journey Analytics Platforms: These platforms track and analyze customer interactions across multiple touchpoints – from initial website visit to final purchase – providing a detailed understanding of the entire customer journey and opportunities for optimization. Predictive Analytics Platforms: Leveraging Artificial Intelligence (AI) and Machine Learning (ML), these platforms forecast future trends, predict customer behavior, and enable proactive, data-driven decision-making for improved marketing effectiveness and customer retention. Recent developments include: January 2022:- The IBM Institute for Business Value, in association with the National Retail Federation, the world’s biggest retail trade association, released their second study, “Consumers want it all,” which reveals increasing consumer preferences for sustainability and shopping journeys splintered across multiple digital, physical, and mobile touchpoints., November 2021:- Tableau Software announced the release of new and future innovations across its data analytics and business intelligence portfolio., March 2021:- Piano, the subscription commerce & customer experience platform, announced today it has joined forces with AT Internet, a France-based leader in digital analytics and contextual data.. Key drivers for this market are: INCREASE IN DEMAND FOR ONLINE SHOPPING. Potential restraints include: Reducing costs associated with maintaining on-premise infrastructure. Notable trends are: SHIFT TOWARDS DATA-DRIVEN BUSINESSES OPERATIONS.