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.

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.

Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

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.

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