The volume, complexity, and irregularity of computational data in modern algorithms and simulations necessitates an unorthodox approach to computing. Understanding the facets and possibilities of soft computing algorithms is necessary for the accurate and timely processing of complex data. Research Advances in the Integration of Big Data and Smart Computing builds on the available literature in the realm of Big Data while providing further research opportunities in this dynamic field. This publication provides the resources necessary for technology developers, scientists, and policymakers to adopt and implement new paradigms in computational methods across the globe. The chapters in this publication advance the body of knowledge on soft computing techniques through topics such as transmission control protocol for mobile ad hoc networks, feature extraction, comparative analysis of filtering techniques, big data in economic policy, and advanced dimensionality reduction methods.

An essential collection of essays for librarians looking to support E-science programs and capabilities to their institutions. From the frontiers of contemporary information science research comes this helpful and timely volume for libraries preparing for the deluge of data that E-science can deliver to their patrons and institutions. The Data Deluge: Can Libraries Cope with E-Science? brings together nine of the world's foremost authorities on the capabilities and requirements of E-science, offering their perspectives to librarians hoping to develop similar programs for their own institutions. The essays contained in The Data Deluge were adapted from papers first delivered at the prestigious annual Library Round Table at the Kanazawa Institute of Technology, where E-science has been the theme from the past two annual conferences. Now this groundbreaking work is available in convenient printed format for the first time. The essays are divided into three parts: an overview of E-science challenges for libraries; perspectives on E-science; and perspectives from individual research libraries.

Unter "Grid Computing" versteht man die gleichzeitige Nutzung vieler Computer in einem Netzwerk für die Lösung eines einzelnen Problems. Grundsätzliche Aspekte und anwendungsbezogene Details zu diesem Gebiet finden Sie in diesem Band. - Grid Computing ist ein viel versprechender Trend, denn man kann damit (1) vorhandene Computer-Ressourcen kosteneffizient nutzen, (2) Probleme lösen, für die enorme Rechenleistungen erforderlich sind, und (3) Synergieeffekte erzielen, auch im globalen Maßstab - Ansatz ist in Forschung und Industrie (IBM, Sun, HP und andere) zunehmend populär (aktuelles Beispiel: Genomforschung) - Buch deckt Motivationen zur Einführung von Grids ebenso ab wie technologische Grundlagen und ausgewählte Beispiele für moderne Anwendungen

Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication.

A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry

A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors' tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you'll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.

Featuring contributions from a diverse set of experts, this thought-provoking book offers a visionary introduction to the computational turn in law and the resulting emergence of the computational legal studies field. It explores how computational data creation, collection, and analysis techniques are transforming the way in which we comprehend and study the law, and the implications that this has for the future of legal studies.

Residents in Boston, Massachusetts are automatically reporting potholes and road hazards via their smartphones. Progressive Insurance tracks real-time customer driving patterns and uses that information to offer rates truly commensurate with individual safety. Google accurately predicts local flu outbreaks based upon thousands of user search queries. Amazon provides remarkably insightful, relevant, and timely product recommendations to its hundreds of millions of customers. Quantcast lets companies target precise audiences and key demographics throughout the Web. NASA runs contests via gamification site TopCoder, awarding prizes to those with the most innovative and cost-effective solutions to its problems. Explorys offers penetrating and previously unknown insights into healthcare behavior. How do these organizations and municipalities do it? Technology is certainly a big part, but in each case the answer lies deeper than that. Individuals at these organizations have realized that they don't have to be Nate Silver to reap massive benefits from today's new and emerging types of data. And each of these organizations has embraced Big Data, allowing them to make astute and otherwise impossible observations, actions, and predictions. It's time to start thinking big. In Too Big to Ignore, recognized technology expert and award-winning author Phil Simon explores an unassailably important trend: Big Data, the massive amounts, new types, and multifaceted sources of information streaming at us faster than ever. Never before have we seen data with the volume, velocity, and variety of today. Big Data is no temporary blip of fad. In fact, it is only going to intensify in the coming years, and its ramifications for the future of business are impossible to overstate. Too Big to Ignore explains why Big Data is a big deal. Simon provides commonsense, jargon-free advice for people and organizations looking to understand and leverage Big Data. Rife with case studies, examples, analysis, and quotes from real-world Big Data practitioners, the book is required reading for chief executives, company owners, industry leaders, and business professionals.

The future of smart cities has arrived, courtesy of citizens and their phones. To prove it, Daniel T. O’Brien explains the transformative insights gleaned from years researching Boston’s 311 reporting system, a sophisticated city management tool that has revolutionized how ordinary Bostonians use and maintain public spaces. Through its phone service, mobile app, website, and Twitter account, 311 catalogues complaints about potholes, broken street lights, graffiti, litter, vandalism, and other issues that are no one citizen’s responsibility but affect everyone’s quality of life. The Urban Commons offers a pioneering model of what modern digital data and technology can do for cities like Boston that seek both prosperous growth and sustainability. Analyzing a rich trove of data, O’Brien discovers why certain neighborhoods embrace the idea of custodianship and willingly invest their time to monitor the city’s common environments and infrastructure. On the government’s side of the equation, he identifies best practices for implementing civic technologies that engage citizens, for deploying public services in collaborative ways, and for utilizing the data generated by these efforts. Boston’s 311 system has narrowed the gap between residents and their communities, and between constituents and local leaders. The result, O’Brien shows, has been the creation of more effective policy and practices that reinvigorate the way citizens and city governments approach their mutual interests. By unpacking when, why, and how the 311 system has worked for Boston, The Urban Commons reveals the power and potential of this innovative system, and the lessons learned that other cities can adapt.