The Science Of Real Time Data Capture
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| Author | : Arthur Stone |
| Publisher | : Oxford University Press |
| Total Pages | : 411 |
| Release | : 2007-04-19 |
| Genre | : Medical |
| ISBN | : 0195346319 |
The National Cancer Institute (NCI) has designated the topic of real-time data capture as an important and innovative research area. As such, the NCI sponsored a national meeting of distinguished research scientists to discuss the state of the science in this emerging and burgeoning field. This book reflects the findings of the conference and discusses the state of the science of real-time data capture and its application to health and cancer research. It provides a conceptual framework for minute-by-minute data capture- ecological momentary assessments (EMA)- and discusses health-related topics where these assessements have been applied. In addition, future directions in real-time data capture assessment, interventions, methodology, and technology are discussed. Despite the rapidly growing interest in the methodology of real-time data capture (e.g. journal special issues, widely attended conference presentations, etc.), to date no single book has focused solely on this topic. The volume will serve as an important resource for researchers, students, and government scientists interested in pursuing real-time health research, and will nicely complement our lists in epidemiology, public health, and oncology.
| Author | : Arthur Stone |
| Publisher | : Oxford University Press |
| Total Pages | : 411 |
| Release | : 2007-04-19 |
| Genre | : Medical |
| ISBN | : 0195178718 |
The National Cancer Institute (NCI) has designated the topic of real-time data capture as an important and innovative research area. As such, the NCI sponsored a national meeting of distinguished research scientists to discuss the state of the science in this emerging and burgeoning field. This book reflects the findings of the conference and discusses the state of the science of real-time data capture and its application to health and cancer research. It provides a conceptual framework for minute-by-minute data capture- ecological momentary assessments (EMA)- and discusses health-related topics where these assessements have been applied. In addition, future directions in real-time data capture assessment, interventions, methodology, and technology are discussed.Despite the rapidly growing interest in the methodology of real-time data capture (e.g. journal special issues, widely attended conference presentations, etc.), to date no single book has focused solely on this topic. The volume will serve as an important resource for researchers, students, and government scientists interested in pursuing real-time health research, and will nicely complement our lists in epidemiology, public health, and oncology.
| Author | : James Warren |
| Publisher | : Simon and Schuster |
| Total Pages | : 481 |
| Release | : 2015-04-29 |
| Genre | : Computers |
| ISBN | : 1638351104 |
Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth
| Author | : Mike Loukides |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 37 |
| Release | : 2018-07-25 |
| Genre | : Computers |
| ISBN | : 1492078212 |
As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.
| Author | : Arthur A. Stone |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : Human experimentation in medicine |
| ISBN | : 9780197708552 |
With contributions from top researchers, this text examines real-time data capture (RTDC) techniques in medical research. It discusses the concepts behind RTDC and how to implement it and analyse the resulting data.
| Author | : |
| Publisher | : |
| Total Pages | : 418 |
| Release | : 1991 |
| Genre | : Aeronautics |
| ISBN | : |
| Author | : Wu Fan |
| Publisher | : Trans Tech Publications Ltd |
| Total Pages | : 7835 |
| Release | : 2011-11-22 |
| Genre | : Technology & Engineering |
| ISBN | : 3038137588 |
Selected, peer reviewed papers from the 2011 International Conference on Manufacturing Science and Technology, (ICMST 2011), September 16-18, 2011, Singapore
| Author | : Mashrur Chowdhury |
| Publisher | : Elsevier |
| Total Pages | : 346 |
| Release | : 2017-04-05 |
| Genre | : Business & Economics |
| ISBN | : 0128098511 |
Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning. - Includes case studies in each chapter that illustrate the application of concepts covered - Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies - Contains contributors from both leading academic and commercial researchers - Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications
| Author | : Avrim Blum |
| Publisher | : Cambridge University Press |
| Total Pages | : 433 |
| Release | : 2020-01-23 |
| Genre | : Computers |
| ISBN | : 1108617360 |
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
| Author | : Agency for Healthcare Research and Quality/AHRQ |
| Publisher | : Government Printing Office |
| Total Pages | : 385 |
| Release | : 2014-04-01 |
| Genre | : Medical |
| ISBN | : 1587634333 |
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
