Imagine you want to renovate your house. Excited, you draw up your own designs, knock it to the ground, and build completely based on your own preferences and gut instincts. Unless you are an engineer, architect, interior designer, plumber, electrician (and more) all in one, you’ll probably end up with something that doesn’t function in […]

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.

This dataset is about book subjects. It has 6 rows and is filtered where the books is The joy of Dreamweaver MX : recipes for data-driven Web sites. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Consumer behavior has changed due to digitization. Online shoppers now refer to user reviews containing comprehensive data produced in real-time, which can be used to determine users’ needs. This paper combines Kansei engineering and natural language processing techniques to extract information on users’ needs from online reviews and provide guidance for subsequent product improvements and development. A crawler tool was used to collect a large number of online reviews for a target product. Frequency analysis was then applied to the text to filter out the product components worth analyzing. The results were categorized and aggregated by experts before sentiment analysis was performed on statements containing the selected adjectives. Finally, the user needs identified could be inputted to Kansei engineering for further product design. This paper verifies the merit of the above method when applied to the mountain bike product category on Amazon. The method proved to be a quick and efficient way to attain accurate product evaluations from end-users and thus represents a feasible approach to intelligently determining user preferences.

The Overview Bell Helmets is a leading outdoor brand best known for its power sports and cycling helmets. The Challenge At the outset of the engagement, Bell had recently completed a brand redesign which included an overhaul to their website. As with any brand focused redesign, a pretty site doesn’t necessarily mean a high converting […]

This project utilised web scraping and topic modelling to take a data driven approach to exploring a support forum for those experiencing difficulties with gambling. Below is an Rdata file that can be load into R and will give both the script to webscrape the forum and the annoymised data from the analyses.

Data-Driven Redesign Let our expert team of researchers, strategists, and designers analyze your customer experience and design you a beautiful site that converts. SCHEDULE AN INTRODUCTORY CALL TRUSTED BY: Launch your new website with confidence. Our Data-Driven Redesign analyzes your customer journey and identifies opportunities for increased conversions before you launch a new site. Beyond […]

The Digital Marketing Analytics Software market is experiencing robust growth, driven by the increasing need for businesses to measure and optimize their marketing campaigns across various digital channels. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors: the escalating adoption of digital marketing strategies across all business sizes, the rising demand for data-driven decision-making, and the increasing sophistication of analytics tools capable of handling vast datasets and providing actionable insights. The market is segmented by deployment type (web-based, installed, iOS, Android) and user base (large, medium, and small enterprises). Web-based solutions dominate due to their accessibility and scalability, while large enterprises represent the largest revenue segment owing to their higher budgets and complex marketing needs. Key players like Google, Adobe, and Oracle are continuously innovating to enhance their offerings, incorporating AI and machine learning capabilities to provide more predictive and insightful analytics. The competitive landscape is dynamic, with both established players and emerging startups vying for market share. Geographic regions like North America and Europe currently hold a significant market share, but Asia-Pacific is expected to show substantial growth in the coming years, driven by increasing internet penetration and digital marketing adoption. Growth restraints include the complexity of implementing and integrating analytics tools, the need for skilled personnel to interpret the data, and concerns around data privacy and security. The continuous evolution of digital marketing strategies necessitates ongoing investments in advanced analytics. The market's growth trajectory is expected to remain strong, propelled by the increasing reliance on data-driven decision-making and the emergence of innovative analytics solutions that offer real-time insights and predictive modeling capabilities. The ongoing development of artificial intelligence and machine learning within the software will further enhance its capabilities and value proposition for businesses of all sizes, ultimately fueling market expansion across all segments and geographic regions. The focus on enhancing user experience and streamlining data visualization will further contribute to the market's robust growth during the forecast period. Companies are increasingly investing in sophisticated analytics solutions to gain a competitive edge, driving further market expansion.

Transformation rules for user interface.

The global web analytics software market is experiencing robust growth, driven by the increasing adoption of digital strategies across industries and the rising need for data-driven decision-making. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors, including the growing prevalence of e-commerce, the proliferation of mobile devices, and the increasing sophistication of analytics tools. Businesses of all sizes are leveraging web analytics to understand user behavior, optimize marketing campaigns, improve website performance, and ultimately enhance customer experience and revenue generation. The cloud-based segment currently dominates the market due to its scalability, cost-effectiveness, and accessibility, while large enterprises represent a significant portion of the overall revenue due to their higher budgets and complex analytical needs. However, growing adoption among SMEs is also a significant contributor to market growth. Competitive pressures and ongoing innovation within the industry are driving continuous improvement in analytics capabilities, including enhanced AI and machine learning integration for more predictive analysis. Despite the positive outlook, the market faces challenges, including data security concerns, the complexity of implementing and utilizing advanced analytics solutions, and the potential for data overload and interpretation difficulties. The increasing regulatory landscape regarding data privacy (e.g., GDPR) also presents a constraint to market growth, requiring vendors to ensure compliance and build trust with customers. Nevertheless, the ongoing demand for data-driven insights across all sectors is expected to outweigh these challenges, ensuring sustained market expansion throughout the forecast period. The competitive landscape is populated by both established players (e.g., Google, SEMrush) and smaller, specialized providers, fostering innovation and driving down prices, making web analytics accessible to a wider range of businesses.

The goal of this study was to identify and evaluate case management and supervision strategies most effective in achieving successful parole outcomes. As part of an evaluation of 3 new supervision practice protocols on a cohort of parolees, a total of 1,964 parolees who began supervision in Georgia between March 1, 2007, and December 31, 2007, were assigned to experimental and control groups. Parolees in the experimental group were subjected to the new practice protocols: positive drug test resulting in a referral to treatment, job loss followed by referral to an employment program and increased contact, and chronic technical violations responded to with an administrative hearing and enrollment in a cognitive skills program. In the control sites, the parole officer's response to a parolee's violation or success was still defined by the Board's Behavior Response and Adjustment Guide (BRAG). Each parolee was tracked for a 12-month follow-up period. The research team collected data on the parolees using two Georgia Department of Corrections and Board of Pardons and Paroles agency databases: the Offender Tracking Information System (OTIS) and the Parole Field Log of Interaction Data (FLOID) database. Outcomes during a 12-month follow-up period were defined as employment, general violations of the conditions of supervision, positive drug tests, arrests for technical violations, misdemeanor and felony arrests, and revocation of parole. The dataset includes a total of 218 variables including supervision protocol variables, demographic and other parolee characteristics variables, offense and parole supervision variables, drug test results, employment activity, as well as arrests, violations, sanctions, programs, and referrals information.

This dataset includes the evaluation data for the Paper "The Trilemma of Large-Data Availability in Web-based Testbeds".
Unfortunately, Zenedo does not allow dataset being larger than 50 GB. Thus, all measurements are included, but not all scenarios are published within this dataset.

File Names follow the naming of:

Comparison of state-of-the-art approaches with the proposed work.

The global market for Marketing Data Analysis Software is experiencing robust growth, driven by the increasing need for data-driven decision-making among businesses across diverse sectors. The market's expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the proliferation of marketing data from various sources (website analytics, social media, CRM systems, etc.), and the growing demand for improved marketing ROI. Businesses are increasingly leveraging these software solutions to gain deeper insights into customer behavior, campaign performance, and market trends, enabling them to optimize their marketing efforts and achieve better results. The retail and eCommerce sectors are currently leading the adoption, followed closely by banking and insurance, and media & entertainment. However, growth is expected across all segments as businesses recognize the value of sophisticated data analysis for competitive advantage. The market is segmented by software type, with website analysis software holding a significant share, but customer service and data analysis software are experiencing rapid growth due to the increasing focus on personalized customer experiences and advanced analytics capabilities. The competitive landscape is dynamic, with established players like HubSpot and Semrush alongside innovative startups. The market's maturity varies across regions; North America currently holds a significant market share due to early adoption and technological advancements, but Asia Pacific is expected to witness substantial growth in the coming years, driven by rapid digitalization and increasing internet penetration. This growth trajectory points toward a substantial increase in market value over the next decade, as more companies integrate data-driven strategies into their core business operations. The forecast period of 2025-2033 presents significant opportunities for market expansion. While North America and Europe maintain strong positions, the Asia-Pacific region is poised for rapid growth, fueled by increasing digital adoption and a burgeoning middle class. However, challenges remain, including the complexity of data integration from diverse sources, the need for skilled data analysts to interpret results effectively, and the rising concerns regarding data privacy and security. Furthermore, the cost of implementing and maintaining these software solutions can be a barrier to entry for smaller businesses. Nevertheless, the overall market outlook remains positive, with consistent growth projected through 2033. The continued innovation in areas like artificial intelligence (AI) and machine learning (ML) will further enhance the capabilities of marketing data analysis software, driving increased adoption and market value.

Note:- Only publicly available data can be worked upon

In today's ever-evolving Ecommerce landscape, success hinges on the ability to harness the power of data. APISCRAPY is your strategic ally, dedicated to providing a comprehensive solution for extracting critical Ecommerce data, including Ecommerce market data, Ecommerce product data, and Ecommerce datasets. With the Ecommerce arena being more competitive than ever, having a data-driven approach is no longer a luxury but a necessity.

APISCRAPY's forte lies in its ability to unearth valuable Ecommerce market data. We recognize that understanding the market dynamics, trends, and fluctuations is essential for making informed decisions.

APISCRAPY's AI-driven ecommerce data scraping service presents several advantages for individuals and businesses seeking comprehensive insights into the ecommerce market. Here are key benefits associated with their advanced data extraction technology:

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To navigate the complexities of the Ecommerce world, you need access to robust datasets. APISCRAPY's commitment to providing comprehensive Ecommerce datasets ensures businesses have the raw materials required for effective decision-making.

Our primary focus is on Amazon data, offering businesses a wealth of information to optimize their Amazon presence. By doing so, we empower our clients to refine their strategies, enhance their products, and make data-backed decisions.

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The digital analytics software market is experiencing robust growth, driven by the increasing reliance on data-driven decision-making across industries. 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. Firstly, the proliferation of digital channels and devices generates massive amounts of data, requiring sophisticated tools for analysis and interpretation. Secondly, businesses are increasingly adopting customer-centric strategies, necessitating a deep understanding of user behavior and preferences, which digital analytics software effectively facilitates. Thirdly, the rise of artificial intelligence (AI) and machine learning (ML) is enhancing the capabilities of these platforms, enabling more accurate predictions and personalized experiences. Major players like Google, Adobe, and IBM are continuously innovating and expanding their offerings, further intensifying competition and driving market expansion. However, challenges remain, including data privacy concerns, the complexity of integrating various data sources, and the need for skilled analysts to interpret the insights generated. Despite these challenges, the long-term outlook for the digital analytics software market remains positive. The ongoing digital transformation across various sectors, coupled with the increasing demand for real-time insights, is expected to maintain the strong growth trajectory. Furthermore, the emergence of new technologies like serverless computing and edge analytics is expected to create new opportunities for innovation and expansion within the market. Segmentation within the market is likely driven by deployment mode (cloud vs. on-premise), functionality (web analytics, mobile analytics, marketing analytics), and industry verticals (e-commerce, retail, healthcare). This diversification will allow for further specialized solutions to cater to specific industry needs and propel market growth throughout the forecast period (2025-2033).

The global web analytics market, valued at $5529.7 million in 2025, is poised for substantial growth. While the provided CAGR is missing, considering the rapid advancements in digital technologies and the increasing reliance on data-driven decision-making across industries, a conservative estimate would place the Compound Annual Growth Rate (CAGR) between 15% and 20% for the forecast period 2025-2033. This growth is fueled by several key drivers: the rising adoption of cloud-based analytics solutions, the increasing demand for real-time data insights, and the growing need for personalized customer experiences. Furthermore, the expansion of e-commerce and the proliferation of mobile devices are significantly contributing to the market's expansion. Emerging trends such as artificial intelligence (AI) and machine learning (ML) integration within web analytics platforms are further enhancing analytical capabilities and driving market growth. While challenges like data privacy concerns and the complexity of integrating diverse data sources exist, the overall market outlook remains positive, suggesting a significant increase in market value by 2033. The competitive landscape is dynamic, with a mix of established players like Adobe, Google, and IBM alongside agile startups like Heap and Mouseflow. These companies offer a range of solutions catering to different business sizes and needs, from basic website traffic analysis to sophisticated predictive analytics. The market is witnessing a shift towards more user-friendly and visually appealing dashboards, making web analytics accessible to a broader range of users beyond dedicated data scientists. This democratization of data, coupled with ongoing technological advancements, promises to further accelerate market growth and consolidate the position of web analytics as a critical component of successful digital strategies across all sectors.

Data ser for a Systematic Literature Review (SLR) of peer-reviewed articles from the Web of Science and Scopus databases, covering the period from January 2021 to February 2025. The review investigates data-driven approaches combined with AI Maching Learning (ML) across four educational domains: learning, teaching, assessment, administration and Cross-cutting applications.

Description

The datasets demonstrate the malware economy and the value chain published in our paper, Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS.

Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts.

We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the Creative Commons Attribution 4.0 International License.

1. MalwareInfectionSet We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB.

2. VictimAccessSet We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB.

3. AccountAccessSet The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB.

Credits Authors

Billy Bob Brumley (Tampere University, Tampere, Finland)

Juha Nurmi (Tampere University, Tampere, Finland)

Mikko Niemelä (Cyber Intelligence House, Singapore)

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS).

Alternative links to download: AccountAccessSet, MalwareInfectionSet, and VictimAccessSet.

Web Analytics Market was valued at USD 6.16 Billion in 2024 and is projected to reach USD 13.6 Billion by 2032, growing at a CAGR of 18.58% from 2026 to 2032.

Web Analytics Market Drivers

Data-Driven Decision Making: Businesses increasingly rely on data-driven insights to optimize their online strategies. Web analytics provides valuable data on website traffic, user behavior, and conversion rates, enabling data-driven decision-making.

E-commerce Growth: The rapid growth of e-commerce has fueled the demand for web analytics tools to track online sales, customer behavior, and marketing campaign effectiveness.

Mobile Dominance: The increasing use of mobile devices for internet browsing has made mobile analytics a crucial aspect of web analytics. Businesses need to understand how users interact with their websites and apps on mobile devices.

analytics tools can be complex to implement and use, requiring technical expertise.