Java Data Object

First published as What is Quantitative Longitudinal Data Analysis?, this title is now available as part of the Bloomsbury Research Methods series.Across the social sciences, there is widespread agree

Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.

Helps readers understand the use of application servers in E-business. This book presents a comprehensive overview of the relevant technologies including CORBA, Java, web servers, and legacy systems.

Categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. This title presents various ways of extracting real-life conclusions from

Academic Writing with Corpora offers a step-by-step accessible guide to using concordancers and aims to help introduce data-driven learning into the academic English classroom.

This groundbreaking book explores the new legal and economic challenges triggered by big data, and analyses the interactions among and between intellectual property, competition law, free speech, priv

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable

Present book covers new paradigms in Blockchain, Big Data and Machine Learning concepts including applications and case studies. It explains dead fusion in realizing the privacy and security of

Two hot topics in recent years are data analytics and AI. Unfortunately, both communities have not done been communicating and collaborating with each other to build the necessary synergies. This book

A wake-up call for America to create a new framework for democratizing data.Public data are foundational to our democratic system. People need consistently high-quality information from trustworthy so

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

In Digital Data Collection and Information Privacy Law, Mark Burdon argues for the reformulation of information privacy law to regulate new power consequences of ubiquitous data collection. Examining developing business models, based on collections of sensor data - with a focus on the 'smart home' - Burdon demonstrates the challenges that are arising for information privacy's control-model and its application of principled protections of personal information exchange. By reformulating information privacy's primary role of individual control as an interrupter of modulated power, Burdon provides a foundation for future law reform and calls for stronger information privacy law protections. This book should be read by anyone interested in the role of privacy in a world of ubiquitous and pervasive data collection.

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

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 no

The Vintage Recordings and Data A5 Journal from Galison features a series of analog images from the past such as a vinyl album, floppy disc, VHS tape, cassette tape, and many more. The journal has sle

Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolu

In the first half of the nineteenth century autobiography became, for the first time, an explicitly commercial genre. Drawing together quantitative data on the Victorian book market, insights from the business ledgers of Victorian publishers and close readings of mid-century novels, Sean Grass demonstrates the close links between these genres and broader Victorian textual and material cultures. This book offers fresh perspectives on major works by Charles Dickens, George Eliot, Mary Elizabeth Braddon, Wilkie Collins and Charles Reade, while also featuring archival research that reveals the volume, diversity, and marketability of Victorian autobiographical texts for the first time. Grass presents life-writing not as a stand-alone genre, but as an integral part of a broader movement of literary, cultural, legal and economic practices through which the Victorians transformed identity into a textual object of capitalist exchange.

Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the Earth. This book provides an advanced overview of digital signal processing (DSP) and its applications to exploration seismology using real-world examples. The book begins by introducing seismic theory, describing how to identify seismic events in terms of signals and noise, and how to convert seismic data into the language of DSP. Deterministic DSP is then covered, together with non-conventional sampling techniques. The final part covers statistical seismic signal processing via Wiener optimum filtering, deconvolution, linear-prediction filtering and seismic wavelet processing. With over sixty end-of-chapter exercises, seismic data sets and data processing MATLAB codes included, this is an ideal resource for electrical engineering students unfamiliar with seismic data, and for Earth Scientists and petroleum pr

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic

The book will start with an examination of the foundations of kdb+/q and will proceed to consider the practicalities of dealing with real high-frequency data, and then demonstrate how kdb+/q can be us

A Practical Approach to Data Structure and Algorithm with Programming in C discusses about how data structure and algorithm plays out with programming in C. This book comprises topics such as algorith

In this book, the founder of object-oriented ontology develops his view that aesthetics is the central discipline of philosophy. Whereas science must attempt to grasp an object in terms of its observa

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-cl

A guide to all aspects of experimental design and data analysis for fMRI experiments, completely revised and updated for the second edition. Functional magnetic resonance imaging (fMRI), which allows

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of ver

One of the "six best books for data geeks" - Financial TimesWith over 200 images and extensive how-to and how-not-to examples, this new edition has everything students and scholars need to u

Engaging and accessible, this book offers students a complete guide to using NVivo for qualitative data analysis. Drawing on their wealth of expertise, the authors offer detailed, practical advice tha

Camera Power is the first book to tackle the policy questions raised by two ongoing revolutions in recording the police: copwatching and police-worn body cameras. Drawing on original research from over 200 jurisdictions and more than 100 interviews - with police leaders and officers, copwatchers, community members, civil rights and civil liberties experts, industry leaders, and technologists - Mary D. Fan offers a vision of the great potential and perils of the growing deluge of audiovisual big data. In contrast to the customary portrayal of big data mining as a threat to civil liberties, Camera Power describes how audiovisual big data analytics can better protect civil rights and liberties and prevent violence in police encounters. With compelling stories and coverage of the most important debates over privacy, public disclosure, proof, and police regulation, this book should be read by anyone interested in how technology is reshaping the relationship with our police.

This timely, expertly written monograph looks at the legal impact that the use of 'Big Data' will have on the provision - and substantive law - of insurance. Insurance companies are set to become some

Now updated to cover latest versions and featuring instructions for both Mac and Windows users, this book is still the go-to source of support for getting to grips with qualitative data anal

This book summarizes recent advances in the work on agenda-setting in a comparative perspective. The book first presents and explains the data-gathering effort undertaken within the Comparative Agenda

This book tries to fill a gap in the financial literature on legal data. It is a practitioner’s guide to this emerging legal data domain in the financial markets context, explaining what is meant by l

This book shows how to analyze spatial and spatial-temporal data. It focuses on key datasets and data analysis, using the open source software WinBUGS, R, and GeoDa. It examines a range of different s

This edited volume on data science features a variety of research ranging from theoretical to applied and computational topics. Aiming to establish the important connection between mathematics and dat

Intelligence and security communities have access to an overwhelming amount of information. More data is better in an information-hungry world, but too much data paralyzes individual and institutional

A timely overview of cutting edge technologies for multimedia retrieval with a special emphasis on scalability The amount of multimedia data available every day is enormous and is growing at an expone

AN UP-TO-DATE, COMPREHENSIVE TREATMENT OF A CLASSIC TEXT ON MISSING DATA IN STATISTICS The topic of missing data has gained considerable attention in recent decades. This new edition by two ac

Indian architecture is not about an object in space; instead it integrates space within the object where the built and the unbuilt become counterpoints to vitalize each other. is engulfed void known

For a couple of years, more and more micro-econometric studies in international trade use data on exports and imports on goods that are based on transaction level data collected by the customs. These

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to