This monograph is a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. Some topics covered are algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection. Included are self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis. Detailed applications are built on a solid scientific basis.
José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Javier Toledo Moreo,Hojjat Adeli
International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings
Author: José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Javier Toledo Moreo,Hojjat Adeli
The two volumes LNCS 10337 and 10338 constitute the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, held in Corunna, Spain, in June 2017. The total of 102 contributions was carefully reviewed and selected from 190 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on natural and artificial computation for biomedicine and neuroscience, addressing topics such as theoretical neural computation; models; natural computing in bioinformatics; physiological computing in affective smart environments; emotions; as well as signal processing and machine learning applied to biomedical and neuroscience applications. The second volume deals with biomedical applications; mobile brain computer interaction; human robot interaction; deep learning; machine learning applied to big data analysis; computational intelligence in data coding and transmission; and applications.
From Sensing and Encrypting to Mining and Modeling
Author: Giorgio Franceschetti,Marina Grossi
Publisher: Artech House
This practical book offers you expert guidance on sensors and the preprocessing of sensed data, the handling of sensed data with secure and safe procedures, and the design, modeling and simulation of complex HS systems. You learn how to store, encrypt and mine sensitive data. Further, the book shows how data is transmitted and received along wired or wireless networks, operating on electromagnetic channels.
Clark Allan Heydon,Roy Levin,Timothy P. Mann,Yuan Yu
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
This is the first book on multivariate analysis to look at large data sets which describes the state of the art in analyzing such data. Material such as database management systems is included that has never appeared in statistics books before.
Sahra Gibbon,Galen Joseph,Jessica Mozersky,Andrea zur Nieden,Sonja Palfner
Author: Sahra Gibbon,Galen Joseph,Jessica Mozersky,Andrea zur Nieden,Sonja Palfner
Category: Social Science
The discovery of the two inherited susceptibility genes BRCA1 and BRCA2 in the mid-1990s created the possibility of predictive genetic testing and led to the establishment of specific medical programmes for those at high risk of developing breast cancer in the UK, US and Europe. In the intervening fifteen years, the medical institutionalisation of these knowledge-practices and accompanying medical techniques for assessing and managing risk have advanced at a rapid pace across multiple national and transnational arenas, whilst also themselves constituting a highly mobile and shifting terrain. This unique edited collection brings together cross-disciplinary social science research to present a broad global comparative understanding of the implications of BRCA gene research and medical practices. With a focus on time-economies that unfold locally, nationally and transnationally (including in Brazil, Canada, France, Germany, India, Italy, the UK and the USA), the essays in this volume facilitate a re-reading of concepts such as prevention, kinship and heredity, and together offer a unique, timely and comparative perspective on these developments. The book provides a coherent structure for examining the diversity of practices and discourses that surround developments linked to BRCA genetics, and to the evolving field of genetics more broadly. It will be of interest to students and scholars of anthropology, sociology, history of science, STS, public health and bioethics. Chapter 8 of this book is freely available as a downloadable Open Access PDF at www.tandfebooks.com/openaccess. It has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 3.0 license.
Illustrates by example the typical steps necessary in computer science to build a mathematical model of any programming paradigm . Presents results of a large and integrated body of research in the area of 'quantitative' program logics.
"Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage. *Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience in the "real-world" application of cutting-edge technology for competitive advantage in industry. Two wave fronts are upon us today: we are being bombarded by an enormous amount of data, and we are confronted by continually increasing technical and business advances. Ideally, the endless stream of data should be one of our major assets. However, this potential asset often tends to overwhelm rather than enrich. Competitive advantage depends on our ability to extract and utilize nuggets of valuable knowledge and insight from this data deluge. The challenges that need to be overcome include the under-utilization of available data due to competing priorities, and the separate and somewhat disparate existing data systems that have difficulty interacting with each other. Conventional approaches to formulating models are becoming progressively more expensive in time and effort. To impart a competitive edge, engineering science in the 21st century needs to augment traditional modelling processes by auto-classifying and self-organizing data; developing models directly from operating experience, and then optimizing the results to provide effective strategies and operating decisions. This approach has wide applicability; in areas ranging from manufacturing processes, product performance and scientific research, to financial and business fields. This monograph explores pattern recognition technology, and its concomitant role in extracting useful knowledge to build technical and business models directly from data, and in optimizing the results derived from these models within the context of delivering competitive industrial advantage. It is not intended to serve as a comprehensive reference source on the subject. Rather, it is based on first-hand experience in the practice of this technology: its development and deployment for profitable application in industry. The technical topics covered in the monograph will focus on the triad of technological areas that constitute the contemporary workhorses of successful industrial application of pattern recognition. These are: systems for self-organising data; data-driven modelling; and genetic algorithms as robust optimizers. "Find it hard to extract and utilise valuable knowledge from the ever-increasing data deluge?" If so, this book will help, as it explores pattern recognition technology and its concomitant role in extracting useful information to build technical and business models to gain competitive industrial advantage. Based on first-hand experience in the practice of pattern recognition technology and its development and deployment for profitable application in Industry. Phiroz Bhagat is often referred to as the pioneer of neural net and pattern recognition technology, and is uniquely qualified to write this book. He brings more than two decades of experience in the "real-world" application of cutting-edge technology for competitive advantage in industry.
David F. Hendry is a seminal figure in modern econometrics. He has pioneered the LSE approach to econometrics, and his influence is wide ranging. This book is a collection of papers dedicated to him and his work. Many internationally renowned econometricians who have collaborated with Hendry or have been influenced by his research have contributed to this volume, which provides a reflection on the recent advances in econometrics and considers the future progress for the methodology of econometrics. Central themes of the book include dynamic modelling and the properties of time series data, model selection and model evaluation, forecasting, policy analysis, exogeneity and causality, and encompassing. The book strikes a balance between econometric theory and empirical work, and demonstrates the influence that Hendry's research has had on the direction of modern econometrics. Contributors include: Karim Abadir, Anindya Banerjee, Gunnar Bårdsen, Andreas Beyer, Mike Clements, James Davidson, Juan Dolado, Jurgen Doornik, Robert Engle, Neil Ericsson, Jesus Gonzalo, Clive Granger, David Hendry, Kevin Hoover, Søren Johansen, Katarina Juselius, Steven Kamin, Pauline Kennedy, Maozu Lu, Massimiliano Marcellino, Laura Mayoral, Grayham Mizon, Bent Nielsen, Ragnor Nymoen, Jim Stock, Pravin Trivedi, Paolo Paruolo, Mark Watson, Hal White, and David Zimmer.
Per Runeson,Martin Host,Austen Rainer,Bjorn Regnell
Author: Per Runeson,Martin Host,Austen Rainer,Bjorn Regnell
Publisher: John Wiley & Sons
Based on their own experiences of in-depth case studies of software projects in international corporations, in this book the authors present detailed practical guidelines on the preparation, conduct, design and reporting of case studies of software engineering. This is the first software engineering specific book on the case study research method.
Empirical Bayes Methods for Estimation, Testing, and Prediction
Author: Bradley Efron
Publisher: Cambridge University Press
We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.
Written by a team of international experts, Extremes and Recurrence in Dynamical Systems presents a unique point of view on the mathematical theory of extremes and on its applications in the natural and social sciences. Featuring an interdisciplinary approach to new concepts in pure and applied mathematical research, the book skillfully combines the areas of statistical mechanics, probability theory, measure theory, dynamical systems, statistical inference, geophysics, and software application. Emphasizing the statistical mechanical point of view, the book introduces robust theoretical embedding for the application of extreme value theory in dynamical systems. Extremes and Recurrence in Dynamical Systems also features: • A careful examination of how a dynamical system can serve as a generator of stochastic processes • Discussions on the applications of statistical inference in the theoretical and heuristic use of extremes • Several examples of analysis of extremes in a physical and geophysical context • A final summary of the main results presented along with a guide to future research projects • An appendix with software in Matlab® programming language to help readers to develop further understanding of the presented concepts Extremes and Recurrence in Dynamical Systems is ideal for academics and practitioners in pure and applied mathematics, probability theory, statistics, chaos, theoretical and applied dynamical systems, statistical mechanics, geophysical fluid dynamics, geosciences and complexity science. VALERIO LUCARINI, PhD, is Professor of Theoretical Meteorology at the University of Hamburg, Germany and Professor of Statistical Mechanics at the University of Reading, UK. DAVIDE FARANDA, PhD, is Researcher at the Laboratoire des science du climat et de l’environnement, IPSL, CEA Saclay, Université Paris-Saclay, Gif-sur-Yvette, France. ANA CRISTINA GOMES MONTEIRO MOREIRA DE FREITAS, PhD, is Assistant Professor in the Faculty of Economics at the University of Porto, Portugal. JORGE MIGUEL MILHAZES DE FREITAS, PhD, is Assistant Professor in the Department of Mathematics of the Faculty of Sciences at the University of Porto, Portugal. MARK HOLLAND, PhD, is Senior Lecturer in Applied Mathematics in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. TOBIAS KUNA, PhD, is Associate Professor in the Department of Mathematics and Statistics at the University of Reading, UK. MATTHEW NICOL, PhD, is Professor of Mathematics at the University of Houston, USA. MIKE TODD, PhD, is Lecturer in the School of Mathematics and Statistics at the University of St. Andrews, Scotland. SANDRO VAIENTI, PhD, is Professor of Mathematics at the University of Toulon and Researcher at the Centre de Physique Théorique, France.
A Practical Guide to Resampling Methods for Testing Hypotheses
Author: Phillip Good
Publisher: Springer Science & Business Media
A step-by-step manual on the application of permutation tests in biology, business, medicine, science, and engineering. Its intuitive and informal style make it ideal for students and researchers, whether experienced or coming to these resampling methods for the first time. The real-world problems of missing and censored data, multiple comparisons, nonresponders, after-the-fact covariates, and outliers are all dealt with at length. This new edition has more than 100 additional pages, and includes streamlined statistics for the k-sample comparison and analysis of variance plus expanded sections on computational techniques, multiple comparisons, multiple regression, comparing variances, and testing interactions in balanced designs. The comprehensive author and subject indexes, plus an expert-system guide to methods, provide for further ease of use, while the exercises at the end of every chapter have been supplemented with drills and a number of graduate-level thesis problems.
Research Methods in Crime and Justice, 2nd Edition, is an innovative text/online hybrid for undergraduate Criminal Justice Research Methods courses. This material uniquely addresses the fundamental teaching issue for this course: how to show students that success as criminal justice practitioners is linked to their acquisition of research skills. Brian Withrow, a widely published academic researcher and former Texas State Trooper, developed this approach for his own undergraduate Research Methods class. He persuasively demonstrates that research skills aren’t just essential to university academic researchers but to successful criminal justice practitioners as well. More than 80 short, sharply focused examples throughout the text rely on research that is conducted by, on behalf of, or relevant to criminal justice practitioners to engage students’ interest like no other text of its kind. Extensive web materials all written by the author provide an array of instructor support material, including a Researcher’s Notebook that provides students (and their instructors) with a series of structured exercises leading to the development of a valid research project. Withrow systematically walks students through defining a question, conducting a literature review, and designing a research method that provides the data necessary to answer the research question—all online, with minimal instructor supervision. The second edition features expanded coverage of measurement, qualitative research methods, and evaluation research methods, as well as additional downloadable journal articles to ensure students begin to think critically about research and can read scholarly literature.
This book is open access under a CC-BY licence. Part of the AHRC/British Library Academic Book of the Future Project, this book interrogates current and emerging contexts of academic books from the perspectives of thirteen expert voices from the connected communities of publishing, academia, libraries, and bookselling.