| data analysis web application: Object Oriented Data Analysis J. S. Marron, Ian L. Dryden, 2021-11-18 Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics and Statistics at Florida International University in Miami, has served as Head of School of Mathematical Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape Analysis. |
| data analysis web application: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data analysis web application: Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality Tyagi, Amit Kumar, 2021-01-08 Augmented and virtual reality (AR and VR) offer exciting opportunities for human computer interaction (HCI), the enhancement of places, and new business cases. Though VR is most popular for video games, especially among younger generations, AR and VR can also be used in applications that include military, medical, navigational, tourism, marketing, and maintenance uses. Research in these technologies along with 3D user interfaces has gained momentum in recent years and has solidified it as a staple technology for the foreseeable future. Multimedia and Sensory Input for Augmented, Mixed, and Virtual Reality includes a collection of business case studies covering a variety of topics related to AR, VR, and mixed reality (MR) including their use in possible applications. This book also touches on the diverse uses of AR and VR in many industries and discusses their importance, challenges, and opportunities. While discussing the use these technologies in sectors such as education, healthcare, and computer science, this book is ideal for computer scientists, engineers, practitioners, stakeholders, researchers, academicians, and students who are interested in the latest research on augmented, mixed, and virtual reality. |
| data analysis web application: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data analysis web application: Web App Development and Real-Time Web Analytics with Python Tshepo Chris Nokeri, 2021-11-20 Learn to develop and deploy dashboards as web apps using the Python programming language, and how to integrate algorithms into web apps. Author Tshepo Chris Nokeri begins by introducing you to the basics of constructing and styling static and interactive charts and tables before exploring the basics of HTML, CSS, and Bootstrap, including an approach to building web pages with HTML. From there, he’ll show you the key Python web frameworks and techniques for building web apps with them. You’ll then see how to style web apps and incorporate themes, including interactive charts and tables to build dashboards, followed by a walkthrough of creating URL routes and securing web apps. You’ll then progress to more advanced topics, like building machine learning algorithms and integrating them into a web app. The book concludes with a demonstration of how to deploy web apps in prevalent cloud platforms. Web App Development and Real-Time Web Analytics with Python is ideal for intermediate data scientists, machine learning engineers, and web developers, who have little or no knowledge about building web apps that implement bootstrap technologies. After completing this book, you will have the knowledge necessary to create added value for your organization, as you will understand how to link front-end and back-end development, including machine learning. What You Will Learn Create interactive graphs and render static graphs into interactive ones Understand the essentials of HTML, CSS, and Bootstrap Gain insight into the key Python web frameworks, and how to develop web applications using them Develop machine learning algorithms and integrate them into web apps Secure web apps and deploy them to cloud platforms Who This Book Is For Intermediate data scientists, machine learning engineers, and web developers. |
| data analysis web application: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| data analysis web application: Innovative Web Applications for Analyzing Traffic Operations Natasha Petrovska, Aleksandar Stevanovic, Borko Furht, 2016-06-17 In response to the need to improve road traffic operation, researchers implement advanced technologies and integration of systems and data, and develop state-of-the-art applications to assist traffic engineers. This SpringerBrief introduces three novel Web applications which can be an exceptional resource and a good visualization tool for traffic operators, managers, and analysts to monitor the congestion, and analyze incidents and signal performance measures. The applications offer more detailed analysis providing users with insights from different levels and perspectives. The benefit of providing these automated and interactive visualization tools is more efficient estimation of the local transport networks’ performance, thus facilitating the decision making process in case of emergency events. |
| data analysis web application: Designing Data-Intensive Web Applications Stefano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, Maristella Matera, 2003-01-04 The most prominent Web applications in use today are data-intensive. Scores of database management systems across the Internet access and maintain large amounts of structured data for e-commerce, on-line trading, banking, digital libraries, and other high-volume sites.Developing and maintaining these data-intensive applications is an especially complex, multi-disciplinary activity, requiring all the tools and techniques that software engineering can provide. This book represents a breakthrough for Web application developers. Using hundreds of illustrations and an elegant intuitive modeling language, the authors—all internationally-known database researchers—present a methodology that fully exploits the conceptual modeling approach of software engineering, from idea to application. Readers will learn not only how to harness the design technologies of relational databases for use on the Web, but also how to transform their conceptual designs of data-intensive Web applications into effective software components.* A fully self-contained introduction and practitioner's guide suitable for both technical and non-technical members of staff, as well as students.* A methodology, development process, and notation (WebML) based on common practice but optimized for the unique challenges of high-volume Web applications.* Completely platform- and product-independent; even the use of WebML is optional.* Based on well-known industry standards such as UML and the Entity Relationship Model.* Enhanced by its own Web site (http://www.webml.org), containing additional examples, papers, teaching materials, developers' resources, and exercises with solutions. |
| data analysis web application: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data analysis web application: Intelligent Data Analysis Michael R. Berthold, David J Hand, 2007-06-07 This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes. |
| data analysis web application: Data Analysis in the Cloud Domenico Talia, Paolo Trunfio, Fabrizio Marozzo, 2015-09-15 Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis. - Introduces data analysis techniques and cloud computing concepts - Describes cloud-based models and systems for Big Data analytics - Provides examples of the state-of-the-art in cloud data analysis - Explains how to develop large-scale data mining applications on clouds - Outlines the main research trends in the area of scalable Big Data analysis |
| data analysis web application: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| data analysis web application: Discrete Data Analysis with R Michael Friendly, David Meyer, 2015-12-16 An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical meth |
| data analysis web application: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| data analysis web application: An Introduction to Categorical Data Analysis Alan Agresti, 2018-10-11 A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences. |
| data analysis web application: Mastering Shiny Hadley Wickham, 2021-04-29 Master the Shiny web framework—and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production |
| data analysis web application: AI Strategies For Web Development Anderson Soares Furtado Oliveira, 2024-09-30 From fundamental to advanced strategies, unlock useful insights for creating innovative, user-centric websites while navigating the evolving landscape of AI ethics and security Key Features Explore AI's role in web development, from shaping projects to architecting solutions Master advanced AI strategies to build cutting-edge applications Anticipate future trends by exploring next-gen development environments, emerging interfaces, and security considerations in AI web development Purchase of the print or Kindle book includes a free PDF eBook Book Description If you're a web developer looking to leverage the power of AI in your projects, then this book is for you. Written by an AI and ML expert with more than 15 years of experience, AI Strategies for Web Development takes you on a transformative journey through the dynamic intersection of AI and web development, offering a hands-on learning experience.The first part of the book focuses on uncovering the profound impact of AI on web projects, exploring fundamental concepts, and navigating popular frameworks and tools. As you progress, you'll learn how to build smart AI applications with design intelligence, personalized user journeys, and coding assistants. Later, you'll explore how to future-proof your web development projects using advanced AI strategies and understand AI's impact on jobs. Toward the end, you'll immerse yourself in AI-augmented development, crafting intelligent web applications and navigating the ethical landscape.Packed with insights into next-gen development environments, AI-augmented practices, emerging realities, interfaces, and security governance, this web development book acts as your roadmap to staying ahead in the AI and web development domain. What you will learn Build AI-powered web projects with optimized models Personalize UX dynamically with AI, NLP, chatbots, and recommendations Explore AI coding assistants and other tools for advanced web development Craft data-driven, personalized experiences using pattern recognition Architect effective AI solutions while exploring the future of web development Build secure and ethical AI applications following TRiSM best practices Explore cutting-edge AI and web development trends Who this book is for This book is for web developers with experience in programming languages and an interest in keeping up with the latest trends in AI-powered web development. Full-stack, front-end, and back-end developers, UI/UX designers, software engineers, and web development enthusiasts will also find valuable information and practical guidelines for developing smarter websites with AI. To get the most out of this book, it is recommended that you have basic knowledge of programming languages such as HTML, CSS, and JavaScript, as well as a familiarity with machine learning concepts. |
| data analysis web application: Intelligent Data analysis and its Applications, Volume II Jeng-Shyang Pan, Vaclav Snasel, Emilio S. Corchado, Ajith Abraham, Shyue-Liang Wang, 2014-06-05 This volume presents the proceedings of the First Euro-China Conference on Intelligent Data Analysis and Applications (ECC 2014), which was hosted by Shenzhen Graduate School of Harbin Institute of Technology and was held in Shenzhen City on June 13-15, 2014. ECC 2014 was technically co-sponsored by Shenzhen Municipal People’s Government, IEEE Signal Processing Society, Machine Intelligence Research Labs, VSB-Technical University of Ostrava (Czech Republic), National Kaohsiung University of Applied Sciences (Taiwan), and Secure E-commerce Transactions (Shenzhen) Engineering Laboratory of Shenzhen Institute of Standards and Technology. |
| data analysis web application: Real-Time Analytics Byron Ellis, 2014-06-23 Construct a robust end-to-end solution for analyzing and visualizing streaming data Real-time analytics is the hottest topic in data analytics today. In Real-Time Analytics: Techniques to Analyze and Visualize Streaming Data, expert Byron Ellis teaches data analysts technologies to build an effective real-time analytics platform. This platform can then be used to make sense of the constantly changing data that is beginning to outpace traditional batch-based analysis platforms. The author is among a very few leading experts in the field. He has a prestigious background in research, development, analytics, real-time visualization, and Big Data streaming and is uniquely qualified to help you explore this revolutionary field. Moving from a description of the overall analytic architecture of real-time analytics to using specific tools to obtain targeted results, Real-Time Analytics leverages open source and modern commercial tools to construct robust, efficient systems that can provide real-time analysis in a cost-effective manner. The book includes: A deep discussion of streaming data systems and architectures Instructions for analyzing, storing, and delivering streaming data Tips on aggregating data and working with sets Information on data warehousing options and techniques Real-Time Analytics includes in-depth case studies for website analytics, Big Data, visualizing streaming and mobile data, and mining and visualizing operational data flows. The book's recipe layout lets readers quickly learn and implement different techniques. All of the code examples presented in the book, along with their related data sets, are available on the companion website. |
| data analysis web application: Web Application Defender's Cookbook Ryan C. Barnett, 2013-01-04 Defending your web applications against hackers and attackers The top-selling book Web Application Hacker's Handbook showed how attackers and hackers identify and attack vulnerable live web applications. This new Web Application Defender's Cookbook is the perfect counterpoint to that book: it shows you how to defend. Authored by a highly credentialed defensive security expert, this new book details defensive security methods and can be used as courseware for training network security personnel, web server administrators, and security consultants. Each recipe shows you a way to detect and defend against malicious behavior and provides working code examples for the ModSecurity web application firewall module. Topics include identifying vulnerabilities, setting hacker traps, defending different access points, enforcing application flows, and much more. Provides practical tactics for detecting web attacks and malicious behavior and defending against them Written by a preeminent authority on web application firewall technology and web application defense tactics Offers a series of recipes that include working code examples for the open-source ModSecurity web application firewall module Find the tools, techniques, and expert information you need to detect and respond to web application attacks with Web Application Defender's Cookbook: Battling Hackers and Protecting Users. |
| data analysis web application: Interaction Flow Modeling Language Marco Brambilla, Piero Fraternali, 2014-11-17 Interaction Flow Modeling Language describes how to apply model-driven techniques to the problem of designing the front end of software applications, i.e., the user interaction. The book introduces the reader to the novel OMG standard Interaction Flow Modeling Language (IFML). Authors Marco Brambilla and Piero Fraternali are authors of the IFML standard and wrote this book to explain the main concepts of the language. They effectively illustrate how IFML can be applied in practice to the specification and implementation of complex web and mobile applications, featuring rich interactive interfaces, both browser based and native, client side components and widgets, and connections to data sources, business logic components and services. Interaction Flow Modeling Language provides you with unique insight into the benefits of engineering web and mobile applications with an agile model driven approach. Concepts are explained through intuitive examples, drawn from real-world applications. The authors accompany you in the voyage from visual specifications of requirements to design and code production. The book distills more than twenty years of practice and provides a mix of methodological principles and concrete and immediately applicable techniques. - Learn OMG's new IFML standard from the authors of the standard with this approachable reference - Introduces IFML concepts step-by-step, with many practical examples and an end-to-end case example - Shows how to integrate IFML with other OMG standards including UML, BPMN, CWM, SoaML and SysML - Discusses how to map models into code for a variety of web and mobile platforms and includes many useful interface modeling patterns and best practices |
| data analysis web application: Applied Missing Data Analysis Craig K. Enders, 2010-04-23 Walking readers step by step through complex concepts, this book translates missing data techniques into something that applied researchers and graduate students can understand and utilize in their own research. Enders explains the rationale and procedural details for maximum likelihood estimation, Bayesian estimation, multiple imputation, and models for handling missing not at random (MNAR) data. Easy-to-follow examples and small simulated data sets illustrate the techniques and clarify the underlying principles. The companion website includes data files and syntax for the examples in the book as well as up-to-date information on software. The book is accessible to substantive researchers while providing a level of detail that will satisfy quantitative specialists. This book will appeal to researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science. It will also serve as a supplemental text for doctoral-level courses or seminars in advanced quantitative methods, survey analysis, longitudinal data analysis, and multilevel modeling, and as a primary text for doctoral-level courses or seminars in missing data. |
| data analysis web application: Practical Web Analytics for User Experience Michael Beasley, 2013-06-21 Practical Web Analytics for User Experience teaches you how to use web analytics to help answer the complicated questions facing UX professionals. Within this book, you'll find a quantitative approach for measuring a website's effectiveness and the methods for posing and answering specific questions about how users navigate a website. The book is organized according to the concerns UX practitioners face. Chapters are devoted to traffic, clickpath, and content use analysis, measuring the effectiveness of design changes, including A/B testing, building user profiles based on search habits, supporting usability test findings with reporting, and more. This is the must-have resource you need to start capitalizing on web analytics and analyze websites effectively. - Discover concrete information on how web analytics data support user research and user-centered design - Learn how to frame questions in a way that lets you navigate through massive amounts of data to get the answer you need - Learn how to gather information for personas, verify behavior found in usability testing, support heuristic evaluation with data, analyze keyword data, and understand how to communicate these findings with business stakeholders |
| data analysis web application: Web Engineering Jordi Cabot, Roberto De Virgilio, Riccardo Torlone, 2017-05-30 This book constitutes the refereed proceedings of the 17th International Conference on Web Engineering, ICWE 2017, held in Rome, Italy, in June 2017. The 20 full research papers and 12 short papers presented together with 6 application papers, 6 demonstration papers, and 6 contributions to the PhD Symposium, were carefully reviewed and selected from 139 submissions. The papers cover research areas such as Web application modeling and engineering, human computation and crowdsourcing applications, Web applications composition and mashup, Social Web applications, Semantic Web applications, Web of Things applications, and big data. |
| data analysis web application: Morgan Kaufmann series in data management systems Stefano Ceri, Piero Fraternali, Aldo Bongio, Marco Brambilla, Sara Comai, Maristella Matera, 2003 This text represents a breakthrough in the process underlying the design of the increasingly common and important data-driven Web applications. |
| data analysis web application: Agile Data Science 2.0 Russell Jurney, 2017-06-07 Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track |
| data analysis web application: Real Time Data Analytics and R Programming Dr.J.Chenni Kumaran, Dr.M.Sivaram, Dr.A.Manimaran, Dr.A.Selvakumar, Dr.S. Ramesh, 2024-02-05 Dr.J.Chenni Kumaran, Professor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.M.Sivaram, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Manimaran, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.A.Selvakumar, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. Dr.S. Ramesh, Profesor, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. |
| data analysis web application: Mining Social Media Lam Thuy Vo, 2019-11-25 BuzzFeed News Senior Reporter Lam Thuy Vo explains how to mine, process, and analyze data from the social web in meaningful ways with the Python programming language. Did fake Twitter accounts help sway a presidential election? What can Facebook and Reddit archives tell us about human behavior? In Mining Social Media, senior BuzzFeed reporter Lam Thuy Vo shows you how to use Python and key data analysis tools to find the stories buried in social media. Whether you're a professional journalist, an academic researcher, or a citizen investigator, you'll learn how to use technical tools to collect and analyze data from social media sources to build compelling, data-driven stories. Learn how to: Write Python scripts and use APIs to gather data from the social web Download data archives and dig through them for insights Inspect HTML downloaded from websites for useful content Format, aggregate, sort, and filter your collected data using Google Sheets Create data visualizations to illustrate your discoveries Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library Apply what you've learned to research topics on your own Social media is filled with thousands of hidden stories just waiting to be told. Learn to use the data-sleuthing tools that professionals use to write your own data-driven stories. |
| data analysis web application: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| data analysis web application: 9th International Conference on Practical Applications of Computational Biology and Bioinformatics Ross Overbeek, Miguel P. Rocha, Florentino Fdez-Riverola, Juan F. De Paz, 2015-05-24 This proceedings presents recent practical applications of Computational Biology and Bioinformatics. It contains the proceedings of the 9th International Conference on Practical Applications of Computational Biology & Bioinformatics held at University of Salamanca, Spain, at June 3rd-5th, 2015. The International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB) is an annual international meeting dedicated to emerging and challenging applied research in Bioinformatics and Computational Biology. Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next generation sequencing technologies, together with novel and ever evolving distinct types of omics data technologies, have put an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information requiring tools from the computational sciences. |
| data analysis web application: Gene Expression Data Analysis Pankaj Barah, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, 2021-11-08 Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences |
| data analysis web application: Storytelling with Data Cole Nussbaumer Knaflic, 2015-10-09 Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! |
| data analysis web application: Disease Surveillance Joseph S. Lombardo, David L. Buckeridge, 2012-11-09 An up-to-date and comprehensive treatment of biosurveillance techniques With the worldwide awareness of bioterrorism and drug-resistant infectious diseases, the need for surveillance systems to accurately detect emerging epidemicsis essential for maintaining global safety. Responding to these issues, Disease Surveillance brings together fifteen eminent researchers in the fields of medicine, epidemiology, biostatistics, and medical informatics to define the necessary elements of an effective disease surveillance program, including research, development, implementation, and operations. The surveillance systems and techniques presented in the text are designed to best utilize modern technology, manage emerging public health threats, and adapt to environmental changes. Following a historical overview detailing the need for disease surveillance systems, the text is divided into the following three parts: Part One sets forth the informatics knowledge needed to implement a disease surveillance system, including a discussion of data sources currently used in syndromic surveillance systems. Part Two provides case studies of modern disease surveillance systems, including cases that highlight implementation and operational difficulties as well as the successes experienced by health departments in the United States, Canada, Europe, and Asia. Part Three addresses practical issues concerning the evaluation of disease surveillance systems and the education of future informatics and disease surveillance practitioners. It also assesses how future technology will shape the field of disease surveillance. This book's multidisciplinary approach is ideal for public health professionals who need to understand all the facets within a disease surveillance program and implement the technology needed to support surveillance activities. An outline of the components needed for a successful disease surveillance system combined with extensive use of case studies makes this book well-suited as a textbook for public health informatics courses |
| data analysis web application: Python for Everybody Charles R. Severance, 2016-04-09 Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled Python for Informatics: Exploring Information.There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course. |
| data analysis web application: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| data analysis web application: Data Analytics Using Splunk 9.x Dr. Nadine Shillingford, 2023-01-20 Make the most of Splunk 9.x to build insightful reports and dashboards with a detailed walk-through of its extensive features and capabilities Key Features Be well-versed with the Splunk 9. x architecture, installation, onboarding, and indexing data features Create advanced visualizations using the Splunk search processing language Explore advanced Splunk administration techniques, including clustering, data modeling, and container management Book DescriptionSplunk 9 improves on the existing Splunk tool to include important features such as federated search, observability, performance improvements, and dashboarding. This book helps you to make the best use of the impressive and new features to prepare a Splunk installation that can be employed in the data analysis process. Starting with an introduction to the different Splunk components, such as indexers, search heads, and forwarders, this Splunk book takes you through the step-by-step installation and configuration instructions for basic Splunk components using Amazon Web Services (AWS) instances. You’ll import the BOTS v1 dataset into a search head and begin exploring data using the Splunk Search Processing Language (SPL), covering various types of Splunk commands, lookups, and macros. After that, you’ll create tables, charts, and dashboards using Splunk’s new Dashboard Studio, and then advance to work with clustering, container management, data models, federated search, bucket merging, and more. By the end of the book, you’ll not only have learned everything about the latest features of Splunk 9 but also have a solid understanding of the performance tuning techniques in the latest version.What you will learn Install and configure the Splunk 9 environment Create advanced dashboards using the flexible layout options in Dashboard Studio Understand the Splunk licensing models Create tables and make use of the various types of charts available in Splunk 9.x Explore the new configuration management features Implement the performance improvements introduced in Splunk 9.x Integrate Splunk with Kubernetes for optimizing CI/CD management Who this book is for The book is for data analysts, Splunk users, and administrators who want to become well-versed in the data analytics services offered by Splunk 9. You need to have a basic understanding of Splunk fundamentals to get the most out of this book. |
| data analysis web application: An Introduction to Statistical Genetic Data Analysis Melinda C. Mills, Nicola Barban, Felix C. Tropf, 2020-02-18 A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website. |
| data analysis web application: Creating Value with Big Data Analytics Peter C. Verhoef, Edwin Kooge, Natasha Walk, 2016-01-08 Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management. |
| data analysis web application: Earth Observation Data Cubes Gregory Giuliani, Gilberto Camara, Brian Killough, Stuart Minchin, 2020-03-16 Satellite Earth observation (EO) data have already exceeded the petabyte scale and are increasingly freely and openly available from different data providers. This poses a number of issues in terms of volume (e.g., data volumes have increased 10× in the last 5 years); velocity (e.g., Sentinel-2 is capturing a new image of any given place every 5 days); and variety (e.g., different types of sensors, spatial/spectral resolutions). Traditional approaches to the acquisition, management, distribution, and analysis of EO data have limitations (e.g., data size, heterogeneity, and complexity) that impede their true information potential to be realized. Addressing these big data challenges requires a change of paradigm and a move away from local processing and data distribution methods to lower the barriers caused by data size and related complications in data management. To tackle these issues, EO data cubes (EODC) are a new paradigm revolutionizing the way users can store, organize, manage, and analyze EO data. This Special Issue is consequently aiming to cover the most recent advances in EODC developments and implementations to broaden the use of EO data to larger communities of users, support decision-makers with timely and actionable information converted into meaningful geophysical variables, and ultimately unlock the information power of EO data. |
| data analysis web application: Big Data Analytics in Earth, Atmospheric and Ocean Sciences Thomas Huang, Tiffany C. Vance, Christopher Lynnes, 2022-11-22 Big Data Analytics in Earth, Atmospheric and Ocean Sciences SPECIAL PUBLICATIONS SERIES Big Data Analytics in Earth, Atmospheric, and Ocean Sciences An ever-increasing volume of Earth data is being gathered. These data are “big” not only in size but also in their complexity, different formats, and varied scientific disciplines. As such, big data are disrupting traditional research. New methods and platforms, such as the cloud, are tackling these new challenges. Big Earth Data Analytics explores new tools for the analysis and display of the rapidly increasing volume of data about the Earth. Volume highlights include: An introduction to the breadth of big earth data analytics Architectures developed to support big earth data analytics Different analysis and statistical methods for big earth data Current applications of analytics to Earth science data Challenges to fully implementing big data analytics The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. |
A Streamlit Web Application for the Analysis of Olympic …
web application that analyses Olympic data using Python programming language has been developed as part of a college project. The application gives users access to data, allowing …
RESEARCH CHALLENGES IN WEB ANALYTICS A STUDY
these data needs in-depth analysis; hence data analytics has become vital. There are different domains have produced different kinds of data. It is necessary to apply the proper techniques, …
OLYMPIC DATA ANALSYSIS WEB APPLICATION - GitHub
The Olympic data analysis web application project is a software application that allows users to interactively analyze and visualize various aspects of the Olympic Games, such as countries, …
Web Application of Olympic data analysis - IJARIIE
The primary objective of this Research paper is to analyse the large Olympic dataset using Exploratory Data Analysis to evaluate the evolution of the Olympic Games over the years.
Web-Based Data Access and Analysis Workbench to …
Mar 31, 2021 · Challenge:Tools for environmental data access and analysis are required to meet needs for consistency, quality, communication, and decision-making. Approach:Web-based …
AN APPLICATION FOR SALES DATA ANALYSIS AND …
the paper is to make a web application that analyzes a company’s sales data of varied products. The main focus here is to read and analyze data to produce insights and the company’s …
PanCanSurvPlot: A Large -scale Pan-cancer Survival …
Dec 25, 2022 · 83 researchers for diverse survival analysis methods and parameters. A web tool that 84 incorporates extensive pan-cancer transcriptomic data and clinical survival information …
ANALYSIS OF THE WEB APPLICATION ON BAYES’ …
This mixed research aims to design and implement the Web Application on Bayes’ Theorem (WABT) in the Statistical Instrumentation for Business subject. WABT presents the procedure …
Analysis of Web application - ijiset.com
The analysis of potential Web application focuses on three important points; (1) What information is to be presented ; (2) What functions are to be performed to the end - user; (3) What …
A Developed Interactive Web Application for Statistical …
this study is to develop a web-based application using the Shiny package in R software, which allows the evaluation of the results of scientific research to be made in a simpler, easier and …
Application of Statistical Analysis Software and Packages for …
CropStat offers several features with a view to analyse data from agricultural field trials and several other sources.
ISSN : 2454-9150 Data Analysis by Web Scraping using …
The software is used to extract data using an application programming interface or as a general-purpose web crawler required by the desired customer.
Web Application Vulnerability Report - Acunetix
Welcome to the 2020 edition of the Acunetix Web Application Vulnerability Report. Every year, Acunetix analyzes data received from Acunetix Online and creates a vulnerability testing …
Static analysis for detecting taint-style vulnerabilities in web ...
In this paper, we address the problem of vulnerable web applications by means of static source code analysis. More precisely, we use flow-sensitive, interprocedural and context-sensitive …
Detecting and Removing Web Application Vulnerabilities with …
Data mining is used to identify false positives using the top 3 machine learning classifiers, and to justify their presence using an induction rule classifier.
Precise Alias Analysis for Static Detection of Web Application ...
In this paper, we address the problem of vulnerable web appli-cations by means of static source code analysis. To this end, we present a novel, precise alias analysis targeted at the unique …
Detecting Web Application Vulnerability using Dynamic …
Inject malicious data into Web applications. Common methods used include: o URL manipulation: use specially crafted parameters to be submitted to the Web application as part of the URL. o …
Saner: Composing Static and Dynamic Analysis to Validate …
More precisely, we combine static and dynamic analysis techniques to identify faulty sanitization procedures that can be bypassed by an at-tacker. We implemented our approach in a tool, …
Web Application Incident Response & Forensics: A Whole …
Web application attacks usually do not leave evidence in the same places as other attacks Web application forensics and incident response requires a solid understanding of web application …
WAFA: Fine-grained Dynamic Analysis of Web Applications
web application PhpBB, a PHP / MySQL-based dynamic web application. 1 Introduction Web applications are one of many kinds of systems with multiple components that dynamically …
A Streamlit Web Application for the Analysis of Olympic …
web application that analyses Olympic data using Python programming language has been developed as part of a college project. The application gives users access to data, allowing …
RESEARCH CHALLENGES IN WEB ANALYTICS A STUDY
these data needs in-depth analysis; hence data analytics has become vital. There are different domains have produced different kinds of data. It is necessary to apply the proper techniques, …
OLYMPIC DATA ANALSYSIS WEB APPLICATION - GitHub
The Olympic data analysis web application project is a software application that allows users to interactively analyze and visualize various aspects of the Olympic Games, such as countries, …
Web Application of Olympic data analysis - IJARIIE
The primary objective of this Research paper is to analyse the large Olympic dataset using Exploratory Data Analysis to evaluate the evolution of the Olympic Games over the years.
Web-Based Data Access and Analysis Workbench to …
Mar 31, 2021 · Challenge:Tools for environmental data access and analysis are required to meet needs for consistency, quality, communication, and decision-making. Approach:Web-based …
AN APPLICATION FOR SALES DATA ANALYSIS AND …
the paper is to make a web application that analyzes a company’s sales data of varied products. The main focus here is to read and analyze data to produce insights and the company’s …
PanCanSurvPlot: A Large -scale Pan-cancer Survival …
Dec 25, 2022 · 83 researchers for diverse survival analysis methods and parameters. A web tool that 84 incorporates extensive pan-cancer transcriptomic data and clinical survival information …
ANALYSIS OF THE WEB APPLICATION ON BAYES’ …
This mixed research aims to design and implement the Web Application on Bayes’ Theorem (WABT) in the Statistical Instrumentation for Business subject. WABT presents the procedure …
Analysis of Web application - ijiset.com
The analysis of potential Web application focuses on three important points; (1) What information is to be presented ; (2) What functions are to be performed to the end - user; (3) What …
A Developed Interactive Web Application for Statistical …
this study is to develop a web-based application using the Shiny package in R software, which allows the evaluation of the results of scientific research to be made in a simpler, easier and …
Application of Statistical Analysis Software and Packages for …
CropStat offers several features with a view to analyse data from agricultural field trials and several other sources.
ISSN : 2454-9150 Data Analysis by Web Scraping using …
The software is used to extract data using an application programming interface or as a general-purpose web crawler required by the desired customer.
Web Application Vulnerability Report - Acunetix
Welcome to the 2020 edition of the Acunetix Web Application Vulnerability Report. Every year, Acunetix analyzes data received from Acunetix Online and creates a vulnerability testing …
Static analysis for detecting taint-style vulnerabilities in web ...
In this paper, we address the problem of vulnerable web applications by means of static source code analysis. More precisely, we use flow-sensitive, interprocedural and context-sensitive …
Detecting and Removing Web Application Vulnerabilities with …
Data mining is used to identify false positives using the top 3 machine learning classifiers, and to justify their presence using an induction rule classifier.
Precise Alias Analysis for Static Detection of Web Application ...
In this paper, we address the problem of vulnerable web appli-cations by means of static source code analysis. To this end, we present a novel, precise alias analysis targeted at the unique …
Detecting Web Application Vulnerability using Dynamic …
Inject malicious data into Web applications. Common methods used include: o URL manipulation: use specially crafted parameters to be submitted to the Web application as part of the URL. o …
Saner: Composing Static and Dynamic Analysis to Validate …
More precisely, we combine static and dynamic analysis techniques to identify faulty sanitization procedures that can be bypassed by an at-tacker. We implemented our approach in a tool, …
Web Application Incident Response & Forensics: A Whole …
Web application attacks usually do not leave evidence in the same places as other attacks Web application forensics and incident response requires a solid understanding of web application …
WAFA: Fine-grained Dynamic Analysis of Web Applications
web application PhpBB, a PHP / MySQL-based dynamic web application. 1 Introduction Web applications are one of many kinds of systems with multiple components that dynamically …
