Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

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Web Development Market Size 2025-2029

The web development market size is forecast to increase by USD 40.98 billion at a CAGR of 10.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing digital transformation across industries and the integration of artificial intelligence (AI) into web applications. This trend is fueled by the need for businesses to enhance user experience, streamline operations, and gain a competitive edge in the market. Furthermore, the rapid evolution of technologies such as Progressive Web Apps (PWAs), serverless architecture, and the Internet of Things (IoT) is creating new opportunities for innovation and expansion. However, this market is not without challenges. The ever-changing technological landscape requires web developers to continuously update their skills and knowledge. Additionally, ensuring web applications are secure and compliant with data protection regulations is becoming increasingly complex.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on building a team of skilled developers, investing in continuous learning and development, and prioritizing security and compliance in their web development projects. By staying abreast of the latest trends and technologies, and adapting quickly to market shifts, organizations can successfully navigate the dynamic the market and drive business growth.

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

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The market continues to evolve at an unprecedented pace, driven by advancements in technology and shifting consumer preferences. Key trends include the adoption of Agile methodologies, DevOps tools, and version control systems for streamlined project management. JavaScript frameworks, such as React and Angular, dominate front-end development, while Magento, Shopify, and WordPress lead in content management and e-commerce. Back-end development sees a rise in Python, PHP, and Ruby on Rails frameworks, enabling faster development and more efficient scalability. Interaction design, user-centered design, and mobile-first design prioritize user experience, while security audits, penetration testing, and disaster recovery solutions ensure website safety.
Marketing automation, email marketing platforms, and CRM systems enhance digital marketing efforts, while social media analytics and Google Analytics provide valuable insights for data-driven decision-making. Progressive enhancement, headless CMS, and cloud migration further expand the market's potential. Overall, the market remains a dynamic, innovative space, with continuous growth fueled by evolving business needs and technological advancements.

How is this Web Development Industry segmented?

The web development 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.

End-user

  Retail and e-commerce
  BFSI
  IT and telecom
  Healthcare
  Others


Business Segment

  SMEs
  Large enterprise


Service Type

  Front-End Development
  Back-End Development
  Full-Stack Development
  E-Commerce Development


Deployment Type

  Cloud-Based
  On-Premises


Technology Specificity

  JavaScript
  Python
  PHP
  Ruby


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The retail and e-commerce segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the digital transformation sweeping various industries. E-commerce and retail sectors lead the market, driven by the increasing preference for online shopping and improved Internet penetration. To cater to this trend, businesses demand user-engaging web applications with smooth navigation, secure payment gateways, and seamless product search and purchase features. Mobile shopping's rise necessitates mobile app development and mobile-optimized websites. Agile development, microservices architecture, and UI/UX design are essential elements in creating engaging and efficient web solutions. Furthermore, AI, machine learning, and data analytics enable data-driven decision making, customer loyalty, and business intelligence.

Web hosting, cloud computing, API integration, and growth hacking are other critical components. Ensuring web accessibility, data security, and e-commerce development is also crucial for businesses in the digital age. Online advertising, email marketing, content strategy, brand building, and data visualization are essential aspects of digital marketing. Serverless computing, u

Unlock the power of ready-to-use data sourced from developer communities and repositories with Developer Community and Code Datasets.

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Developer Community and Code Datasets are a treasure trove of public data points gathered from tech communities and code repositories across the web.

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Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.

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Customer Analytics Applications Market Size 2024-2028

The customer analytics applications market size is estimated to grow by USD 16.73 billion at a CAGR of 17.58% between 2023 and 2028. The growth of the market depends on several factors, including the increasing number of social media users, the growing need for improved customer satisfaction, and an increase in the adoption of customer analytics by SMEs. Customer analytics application refers to a software or system that analyzes customer data such as behavioral, demographic, and personal information to gain insights into their behavior, preferences, and needs. It uses various techniques such as data mining, predictive modeling, and statistical analysis to gather information and make informed decisions in marketing, sales, product development, and overall customer management. The goal of a customer analytics application is to enhance customer understanding and improve business strategies by allowing companies to make data-driven decisions and provide personalized experiences to their customers.

What will be the Size of the Market During the Forecast Period?

To learn more about this report, View Report Sample

Market Dynamics

In the evolving internet retail landscape, businesses are increasingly adopting innovative cloud deployment modes to enhance their operational efficiency. Customer Data Platforms (CDPs) like Neustar and Clarity Insight are pivotal in integrating and analyzing customer data to drive personalized experiences and strategic decisions. These platforms leverage cloud deployment modes to offer scalable solutions that support internet retail operations and enhance customer engagement. Data platforms are instrumental in collecting and processing vast amounts of data, providing valuable insights for trailblazers in the industry. By utilizing advanced cloud deployment modes, companies can efficiently manage their data infrastructure and improve their online retail strategies. Integrating Neustar and Clarity Insight into their systems enables businesses to stay ahead of the competition by offering tailored experiences and optimizing their internet retail performance through scalable solutions.

Key Market Driver

An increase in the adoption of customer analytics by SMEs is notably driving market growth. Expanding the efficiency and performance of business operations is critical to achieving the desired set of goals of an organization. Businesses with a customer-centric approach deal with massive amounts of customer data, which is stored, managed, and processed in real-time. SMEs generate numerous forms of customer data related to customer demographics and sales, marketing campaigns, websites, and conversations. Consequently, these businesses must scrutinize all this customer-related data to achieve a competitive edge in the market. SMEs are majorly using these as they enable better forecasting, resource management, and streamlining of data under one platform, lower operational costs, improve decision-making, and expand sales.

In addition, the increase in customer data, along with the companies' need to automate customer data processing, is leading to the increased adoption by SMEs. Hence, customer analytics is being executed across SMEs for better management of their business operations via a centralized management system with enhanced collaboration, productivity, simplified compliance, and risk management. Such factors are the significant driving factors driving the growth of the global market during the forecast period.

Major Market Trends

Advancements in technology are an emerging trend shaping the market growth. AI and ML technologies have revolutionized the way businesses understand and analyze customer data, allowing them to make more informed decisions and deliver customized experiences. Also, AI and ML have played a critical role in fake detection and prevention in the customer analytics market. Algorithms can identify unusual activities that may indicate fraud by analyzing transactional data and behavioral patterns. This allows businesses to secure themselves and their customers from potential financial losses.

Additionally, AI and ML have enhanced customer segmentation capabilities. Businesses can group customers based on their similarities by using clustering algorithms, allowing them to create targeted marketing campaigns for specific segments. This enables enterprises to personalize their messages and offers, resulting in higher customer engagement and conversion rates. These factors are anticipated to fuel the market growth and trends during the forecast period.

Significant Market Restrain

Data integration issues are a significant challenge hindering market growth. To analyze customer data generated from various types of systems, enterprises use these. The expansion in the use of smart devices and Internet penetration is creating huge amounts of dat

Our Web Scraping dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

For lead generation

With millions of companies from around the globe, this scraped data enables you to filter potential clients based on specific criteria and hasten the conversion process.

Use cases

1. Filter potential clients according to location, size, and other criteria
2. Enrich your existing database
3. Improve conversion rates
4. Use predictive models to identify potential leads
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For market and business analysis

Our Web Scraping Data on companies gives information about millions of businesses, allowing you to evaluate your competitors.

Use cases

1. Know your competitors
2. See your competitors' size, headcount, and revenue
3. Come up with a data-driven strategy for the next quarter

For Investors

We recommend Web Scraping Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global Web Scraping Data.

Use cases

1. Screen startups and industries showing early signs of growth
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4. Predict a startup's potential at the founding moment
5. Choose companies that fit you in terms of size and headcount

For sales prospecting

Web Scraping Data saves time your employees would otherwise use it to find potential clients and choose the best prospects manually.

Use cases

1. Make a short list of the top prospects
2. Define which companies are large or small enough to buy your product
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4. Sort the companies by their distance from your warehouse to draw a line where selling won't result in satisfactory profit

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

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AI Driven Web Scraping Market Size 2025-2029

The AI driven web scraping market size is valued to increase USD 3.16 billion, at a CAGR of 39.4% from 2024 to 2029. Surging demand for data-driven insights and business intelligence will drive the ai driven web scraping market.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Type - Dynamic scraping segment was valued at USD 82.90 billion in 2023
By Application - E-commerce and retail segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 1.00 million
Market Future Opportunities: USD 3159.00 million
CAGR from 2024 to 2029 : 39.4%

Market Summary

The AI-driven web scraping market is experiencing significant growth, fueled by the increasing demand for data-driven insights and business intelligence. The rise of Large Language Model (LLM) and the democratization of web scraping through no-code and low-code platforms are key drivers, enabling businesses to extract valuable data from the web more efficiently and effectively than ever before. These advancements enable businesses to extract valuable data from the web more efficiently and effectively than ever before. However, this growth comes with challenges. The sophistication of anti-scraping technologies is escalating, requiring advanced techniques and technologies to bypass these barriers.
According to recent estimates, the global web scraping market is projected to reach USD12.5 billion by 2027, underscoring its growing importance in the digital business landscape. Despite these challenges, the future of AI-driven web scraping is bright, offering businesses a powerful tool to gain a competitive edge in today's data-driven economy.

What will be the Size of the AI Driven Web Scraping Market during the forecast period?

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How is the AI Driven Web Scraping Market Segmented ?

The ai driven web scraping 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.

Type

  Dynamic scraping
  Static scraping
  API-based scraping


Application

  E-commerce and retail
  Finance and banking
  Market research
  Cyber security
  Others


Deployment

  Cloud-based
  On-premises
  Hybrid


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Type Insights

The dynamic scraping segment is estimated to witness significant growth during the forecast period.

The AI-driven web scraping market continues to evolve, with the services segment, or Data as a Service (DaaS,) gaining significant traction. In this model, clients outsource the entire data acquisition process to specialized companies, specifying their data requirements, including target websites and desired data fields, while the service provider manages the technical aspects. This approach is ideal for organizations lacking the in-house expertise, infrastructure, or time for complex web scraping operations. The integration of artificial intelligence is crucial for scalability and efficiency, enabling distributed scraping systems, data validation rules, and data visualization dashboards. Machine learning models power link extraction techniques, image recognition algorithms, and natural language processing, while proxy server management, unstructured data processing, and data cleaning pipelines ensure legal compliance frameworks.

Data transformation rules and structured data parsing facilitate API integration strategies, and headless browser automation, error handling mechanisms, and rate limiting protocols maintain ethical scraping guidelines. The market's growth is evident in the 50% annual increase in companies using cloud storage solutions for data storage and real-time data streaming.

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The Dynamic scraping segment was valued at USD 82.90 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

North America is estimated to contribute 38% 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.

See How AI Driven Web Scraping Market Demand is Rising in North America Request Free Sample

The market is experiencing significant growth and evolution, with North America leading the charge. This region, particularly the United States, boasts the largest and most mature market due to its advanced technological infrastructure, the presence of leadi

UGC Galleries for Hotel Websites Market Outlook

According to our latest research, the UGC Galleries for Hotel Websites market size reached USD 1.13 billion in 2024, demonstrating robust momentum driven by the hospitality sector’s digital transformation. The market is expected to grow at a CAGR of 12.4% from 2025 to 2033, forecasting a value of USD 3.24 billion by 2033. This growth is primarily fueled by hotels’ increasing focus on leveraging user-generated content (UGC) to enhance guest engagement, build trust, and drive direct bookings through their websites.

The rapid adoption of digital solutions across the hospitality industry is a significant growth driver for the UGC Galleries for Hotel Websites market. Hotels are increasingly recognizing the power of authentic guest experiences shared through photos, videos, and reviews. Such content not only enriches the website’s visual appeal but also acts as social proof, influencing potential guests’ booking decisions. As consumers become more discerning and seek personalized travel experiences, UGC galleries provide a compelling way for hotels to showcase real guest stories, resulting in higher conversion rates and improved brand reputation. The proliferation of social media platforms and the ease of content sharing have further amplified the volume and impact of UGC, making it an indispensable marketing tool for hotels of all sizes.

Another pivotal factor contributing to the market’s expansion is the growing demand for seamless and integrated digital experiences. Modern travelers expect hotel websites to be interactive, visually engaging, and reflective of genuine guest experiences. UGC galleries, powered by advanced software and AI-driven content curation, enable hotels to aggregate and display high-quality guest content in real time. This not only fosters a sense of community but also provides potential guests with a transparent view of the property, amenities, and services. The integration of UGC galleries with booking engines and loyalty programs further enhances user engagement and encourages repeat visits. As hotels strive to differentiate themselves in a competitive landscape, investment in UGC gallery solutions is becoming a strategic priority.

The emergence of advanced analytics and personalization technologies is also shaping the UGC Galleries for Hotel Websites market. Hotels can now leverage data-driven insights to curate and display the most relevant and impactful user-generated content, tailored to specific audience segments. This targeted approach maximizes the effectiveness of UGC galleries, driving higher engagement and conversion rates. Additionally, the use of moderation tools ensures that only high-quality, brand-appropriate content is showcased, mitigating reputational risks. The continuous evolution of these technologies, coupled with increasing internet penetration and smartphone usage, is expected to sustain the market’s upward trajectory over the forecast period.

From a regional perspective, North America currently leads the UGC Galleries for Hotel Websites market, accounting for the largest share due to the high concentration of technologically advanced hotels and early adoption of digital marketing solutions. Europe follows closely, driven by a vibrant tourism industry and stringent focus on enhancing guest experiences. The Asia Pacific region is poised for the fastest growth, fueled by rising internet usage, expanding hospitality sectors in countries like China and India, and increasing investments in digital infrastructure. Latin America and the Middle East & Africa are also witnessing steady adoption, supported by growing tourism and hospitality investments. Regional market dynamics are influenced by factors such as travel trends, regulatory frameworks, and the level of digital maturity among hotels.

Component Analysis

The Component segment of the UGC Galleries for Hotel Websites market is bifurcated into software and services, each playing a cru

The Reddit Subreddit Dataset by Dataplex offers a comprehensive and detailed view of Reddit’s vast ecosystem, now enhanced with appended AI-generated columns that provide additional insights and categorization. This dataset includes data from over 2.1 million subreddits, making it an invaluable resource for a wide range of analytical applications, from social media analysis to market research.

Dataset Overview:

This dataset includes detailed information on subreddit activities, user interactions, post frequency, comment data, and more. The inclusion of AI-generated columns adds an extra layer of analysis, offering sentiment analysis, topic categorization, and predictive insights that help users better understand the dynamics of each subreddit.

2.1 Million Subreddits with Enhanced AI Insights: The dataset covers over 2.1 million subreddits and now includes AI-enhanced columns that provide: - Sentiment Analysis: AI-driven sentiment scores for posts and comments, allowing users to gauge community mood and reactions. - Topic Categorization: Automated categorization of subreddit content into relevant topics, making it easier to filter and analyze specific types of discussions. - Predictive Insights: AI models that predict trends, content virality, and user engagement, helping users anticipate future developments within subreddits.

Sourced Directly from Reddit:

All social media data in this dataset is sourced directly from Reddit, ensuring accuracy and authenticity. The dataset is updated regularly, reflecting the latest trends and user interactions on the platform. This ensures that users have access to the most current and relevant data for their analyses.

Key Features:

• Subreddit Metrics: Detailed data on subreddit activity, including the number of posts, comments, votes, and user participation.
• User Engagement: Insights into how users interact with content, including comment threads, upvotes/downvotes, and participation rates.
• Trending Topics: Track emerging trends and viral content across the platform, helping you stay ahead of the curve in understanding social media dynamics.
• AI-Enhanced Analysis: Utilize AI-generated columns for sentiment analysis, topic categorization, and predictive insights, providing a deeper understanding of the data.

Use Cases:

• Social Media Analysis: Researchers and analysts can use this dataset to study online behavior, track the spread of information, and understand how content resonates with different audiences.
• Market Research: Marketers can leverage the dataset to identify target audiences, understand consumer preferences, and tailor campaigns to specific communities.
• Content Strategy: Content creators and strategists can use insights from the dataset to craft content that aligns with trending topics and user interests, maximizing engagement.
• Academic Research: Academics can explore the dynamics of online communities, studying everything from the spread of misinformation to the formation of online subcultures.

Data Quality and Reliability:

The Reddit Subreddit Dataset emphasizes data quality and reliability. Each record is carefully compiled from Reddit’s vast database, ensuring that the information is both accurate and up-to-date. The AI-generated columns further enhance the dataset's value, providing automated insights that help users quickly identify key trends and sentiments.

Integration and Usability:

The dataset is provided in a format that is compatible with most data analysis tools and platforms, making it easy to integrate into existing workflows. Users can quickly import, analyze, and utilize the data for various applications, from market research to academic studies.

User-Friendly Structure and Metadata:

The data is organized for easy navigation and analysis, with metadata files included to help users identify relevant subreddits and data points. The AI-enhanced columns are clearly labeled and structured, allowing users to efficiently incorporate these insights into their analyses.

Ideal For:

• Data Analysts: Conduct in-depth analyses of subreddit trends, user engagement, and content virality. The dataset’s extensive coverage and AI-enhanced insights make it an invaluable tool for data-driven research.
• Marketers: Use the dataset to better understand your target audience, tailor campaigns to specific interests, and track the effectiveness of marketing efforts across Reddit.
• Researchers: Explore the social dynamics of online communities, analyze the spread of ideas and information, and study the impact of digital media on public discourse, all while leveraging AI-generated insights.

This dataset is an essential resource for anyone looking to understand the intricacies of Reddit's vast ecosystem, offering the data and AI-enhanced insights needed to drive informed decisions and strategies across various fields. Whether you’re tracking emerging trends, analyzing user behavior, or conduc...

MyGeoHub is a science gateway for researchers working with geospatial data. Based on the HUBzero cyberinfrastructure framework, it provides general-purpose software modules enabling geospatial data management, processing and visualization. Termed “GABBs” (Geospatial Data Analysis Building Blocks), these modules can be leveraged to build geospatial data driven tools with minimal programming and construct dynamic workflows chaining both local and remote tools and data sources. We will present examples of such end-to-end workflows demonstrating the underlying software building blocks that have also found use beyond the MyGeoHub gateway in other science domains.

Background For a long time one could not imagine being able to identify species on the basis of genotype only as there were no technological means to do so. But conventional phenotype-based identification requires much effort and a high level of skill, making it almost impossible to analyze a huge number of organisms, as, for example, in microbe-related biological disciplines. Comparative analysis of 16S rRNA has been changing the situation, however. We report here an approach that will allow rapid and accurate phylogenetic comparison of any unknown strain to all known type strains, enabling tentative assignments of strains to species. The approach is based on two main technologies: genome profiling and Internet-based databases. Results A complete procedure for provisional identification of species using only their genomes is presented, using random polymerase chain reaction, temperature-gradient gel electrophoresis, image processing to generate 'species-identification dots' (spiddos) and data processing. A database website for this purpose was also constructed and operated successfully. The protocol was standardized to make the system reproducible and reliable. The overall methodology thus established has remarkable aspects in that it enables non-experts to obtain an initial species identification without a lot of effort and is self-developing; that is, species can be determined more definitively as the database is used more and accumulates more genome profiles. Conclusions We have devised a methodology that enables provisional identification of species on the basis of their genotypes only. It is most useful for microbe-related disciplines as they face the most serious difficulties in species identification.

Escher is a web application for visualizing data on biological pathways. Three key features make Escher a uniquely effective tool for pathway visualization. First, users can rapidly design new pathway maps. Escher provides pathway suggestions based on user data and genome-scale models, so users can draw pathways in a semi-automated way. Second, users can visualize data related to genes or proteins on the associated reactions and pathways, using rules that define which enzymes catalyze each reaction. Thus, users can identify trends in common genomic data types (e.g. RNA-Seq, proteomics, ChIP)—in conjunction with metabolite- and reaction-oriented data types (e.g. metabolomics, fluxomics). Third, Escher harnesses the strengths of web technologies (SVG, D3, developer tools) so that visualizations can be rapidly adapted, extended, shared, and embedded. This paper provides examples of each of these features and explains how the development approach used for Escher can be used to guide the development of future visualization tools.

Can your desktop computer crunch the large GIS datasets that are becoming increasingly common across the geosciences? Do you have access to, or the know how to, take advantage of advanced high performance computing (HPC) capability? Web based cyberinfrastructure takes work off your desk or laptop computer and onto infrastructure or "cloud" based data and processing servers. This talk will describe the HydroShare collaborative environment and web based services being developed to support the sharing and processing of hydrologic data and models. HydroShare supports the storage and sharing of a broad class of hydrologic data including time series, geographic features and rasters, multidimensional space-time data and structured collections of data representing river geometry. Web service tools and a python client library provide researchers with access to high performance computing resources without requiring them to become HPC experts. This reduces the time and effort spent in finding and organizing the data required to prepare the inputs for hydrologic models and facilitates the management of online data and execution of models on HPC systems. This talk will illustrate web and client based use of data services that support the delineation of watersheds to define a modeling domain, then extract terrain and land use information to automatically configure the inputs required for hydrologic models. These services support the Terrain Analysis Using Digital Elevation Model (TauDEM) tools for watershed delineation and generation of hydrology-based terrain information such as wetness index and stream networks. These services also support the derivation of inputs for the Utah Energy Balance snowmelt model used to address questions such as how climate, land cover and land use change may affect snowmelt inputs to runoff generation. These cases serve as examples for how this approach can be extended to other models to enhance the use of web and data services in the geosciences.

Presentation at Kansas University GIS Days November 18, 2015

We provide location data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and location insights. We help marketers, agencies, and platforms build precise location audience segments, optimize location targeting, attribute locations, and understand cross-device journeys. Our continuously updated location datasets deliver real-time location insights that power smarter location-based campaigns and future-ready strategies.

Leverage our location data solutions for the following use cases: - Location Data Validation & Model Building - Cultural & Seasonal Campaign Insights - Targeted, Data-Driven Location Advertising - Travel & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Location Data Attributes - Enrich CRM - Personalize Location Audiences - Fraud Prevention - Location Audience Curation

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Success.ai is dedicated to providing advanced consumer insights that empower businesses to understand and predict consumer behaviors effectively. Our datasets are crafted from diverse online interactions, including keyword searches, sentiment analysis, and web activity, paired with detailed geodemographic data to offer a holistic view of consumer trends.

Utilize Our Consumer Insights to Enhance Your Business Strategies:

• Keyword Data Analysis: Understand what your potential customers are searching for with detailed keyword data. This information is crucial for optimizing SEO strategies and aligning your content with consumer interests.
• Sentiment Analysis: Gauge public opinion and sentiment trends across various demographics to tailor your marketing messages or product features.
• Web Activity Insights: Track how consumers interact online to refine your online marketing strategies and improve user engagement.
• Geodemographic Profiling: Employ detailed demographic and geographic data to segment your marketing campaigns and personalize outreach efforts.
• Consumer Behavior Reports: Analyze consumer purchasing patterns and preferences to forecast future trends and adjust your business approach accordingly.

Why Success.ai Stands Out:

• Tailored Data Solutions: Our data solutions are customized to meet specific industry needs, ensuring relevancy and applicability.
• Real-Time Data Processing: We offer the latest insights with continuous updates, keeping your business ahead of the curve.
• Precision and Compliance: Our data collection methods are not only precise but also strictly adhere to global privacy standards, ensuring ethical usage and data reliability.
• Affordable Pricing: We provide competitive pricing models that guarantee the best value for extensive data insights.

Empower Your Business With Data-Driven Decisions:

• Email Marketing: Utilize our data to craft targeted email campaigns that resonate with specific consumer segments.
• Online Marketing: Enhance your digital presence by aligning your strategies with real-time consumer data insights.
• B2B Lead Generation: Identify and engage potential business clients by understanding their industry-specific behaviors and needs.
• Sales Data Enrichment: Enrich your sales strategies with comprehensive consumer data to boost conversion rates.
• Competitive Intelligence: Stay ahead of the competition by leveraging detailed insights into consumer behaviors and market trends.

With Success.ai, transform vast data into actionable insights that drive business growth and strategic innovation. Connect with us today to learn how our Consumer Insights Data can revolutionize your approach to market analysis and consumer engagement.

Experience the competitive edge with Success.ai, where we don't just offer data; we deliver market leadership.

We provide mobility data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and mobility insights. We help marketers, agencies, and platforms build precise mobility audience segments, optimize mobility targeting, attribute locations, and understand cross-device journeys. Our continuously updated mobility datasets deliver real-time mobility insights that power smarter mobility-based campaigns and future-ready strategies.

Leverage our mobility data solutions for the following use cases: - Mobility Data Validation & Model Building - Cultural & Seasonal Campaign Mobility Insights - Targeted, Data-Driven Mobility Advertising - Travel & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Mobility Data Attributes - Enrich CRM - Personalize Mobility Audiences - Fraud Prevention - Mobility Audience Curation

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We provide interest data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and interest insights to understand what audiences are consuming online. We help marketers, agencies, and platforms build precise interest segments, optimize interest targeting, attribute locations, and understand cross-device audience journeys. Our continuously updated interest datasets deliver real-time interest insights that power smarter campaigns and future-ready strategies.

Leverage our interest data solutions for the following use cases: - Interest Data Validation & Model Building - Cultural & Seasonal Interest Insights - Targeted, Data-Driven Advertising - Interest & Location-Based Targeting - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Augment Interest Data Attributes - Enrich CRM - Personalize Interest Audiences - Fraud Prevention - Interest Audience Curation

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PredictLeads Global Technographic Dataset delivers in-depth insights into technology adoption across millions of companies worldwide. Our dataset, sourced from HTML, JavaScript, and job postings, enables B2B sales, marketing, and data enrichment teams to refine targeting, enhance lead scoring, and optimize outreach strategies. By tracking 48,000+ technologies across 98M+ websites, businesses can uncover market trends, assess competitor technology stacks, and personalize their approach.

Use Cases:

✅ Enhance CRM Data – Enrich company records with detailed real-time technology insights. ✅ Targeted Sales Outreach – Identify prospects based on their tech stack and personalize outreach. ✅ Competitor & Market Analysis – Gain insights into competitor technology adoption and industry trends. ✅ Lead Scoring & Prioritization – Rank potential customers based on adopted technologies. ✅ Personalized Marketing – Craft highly relevant campaigns based on technology adoption trends.

API Attributes & Structure:

• id (string, UUID) – Unique identifier for each technology detection.
• first_seen_at (ISO 8601 date-time) – Timestamp when the technology was first detected on the company's website.
• last_seen_at (ISO 8601 date-time) – Most recent timestamp when the technology was last observed.
• behind_firewall (boolean) – Indicates whether the technology is protected behind a firewall.
• score (float, 0–1) – Confidence score for the detection accuracy.
• company (object) – The company using the detected technology, including:
• - id (UUID) – Unique company identifier.
• - domain (string) – Company website domain.
• - company_name (string) – Company's official name.
• - ticker (string, nullable) – Stock ticker (if publicly listed).
• technology (object) – Information on the detected technology, including:
• - id (UUID) – Unique technology identifier.
• - name (string) – Technology name (e.g., Salesforce, HubSpot, AWS).
• seen_on_job_openings (boolean) – True/False flag indicating if the technology is - mentioned in job postings.
• seen_on_subpages (array of objects) – List of company subpages where the technology was detected.

📌 PredictLeads Technographic Data is trusted by enterprises and B2B professionals for accurate, real-time technology intelligence, enabling smarter prospecting, data-driven marketing, and competitive analysis

PredictLeads Technology Detections Dataset https://docs.predictleads.com/v3/guide/technology_detections_dataset

We provide interest data across the web with 30+ enriched attributes, including demographics, devices, web activity, intent and interest insights to understand what audiences are consuming online. We help marketers, agencies, and platforms build precise interest segments, optimize interest targeting, attribute locations, and understand cross-device audience journeys. Our continuously updated interest datasets deliver real-time interest insights that power smarter campaigns and future-ready strategies.

Leverage our interest data solutions for the following use cases: - Interest Data Validation & Model Building - Cultural & Seasonal Interest Insights - Targeted, Data-Driven Advertising - Interest & Location-Based Targeting - Trial & Partnership Transparency

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For more information, please visit https://www.adpreference.co/