From Data To Models And Back
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| Author | : Juliana Bowles |
| Publisher | : Springer Nature |
| Total Pages | : 233 |
| Release | : 2021-03-04 |
| Genre | : Computers |
| ISBN | : 3030706508 |
This book constitutes the refereed proceedings of the 9th International Symposium on From Data Models and Back, DataMod 2020, held virtually, in October 2020. The 11 full papers and 3 short papers presented in this book were selected from 19 submissions. The papers are grouped in these topical sections: machine learning; simulation-based approaches, and data mining and processing related approaches.
| Author | : Hadley Wickham |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 521 |
| Release | : 2016-12-12 |
| Genre | : Computers |
| ISBN | : 1491910364 |
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
| Author | : Len Silverston |
| Publisher | : John Wiley & Sons |
| Total Pages | : 572 |
| Release | : 2011-08-08 |
| Genre | : Computers |
| ISBN | : 111808232X |
A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM.
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 191 |
| Release | : 2013-09-03 |
| Genre | : Mathematics |
| ISBN | : 0309287812 |
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.
| Author | : Andrew Gelman |
| Publisher | : Cambridge University Press |
| Total Pages | : 654 |
| Release | : 2007 |
| Genre | : Mathematics |
| ISBN | : 9780521686891 |
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
| Author | : Matthew West |
| Publisher | : Elsevier |
| Total Pages | : 408 |
| Release | : 2011-02-07 |
| Genre | : Computers |
| ISBN | : 0123751071 |
Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool. This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling. - Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality - Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templates - Develops ideas for creating consistent approaches to high quality data models
| Author | : Dimitris Bertsimas |
| Publisher | : Ingram |
| Total Pages | : 530 |
| Release | : 2004 |
| Genre | : Business & Economics |
| ISBN | : 9780975914601 |
Combines topics from two traditionally distinct quantitative subjects, probability/statistics and management science/optimization, in a unified treatment of quantitative methods and models for management. Stresses those fundamental concepts that are most important for the practical analysis of management decisions: modeling and evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and allocating scarce resources optimally.
| Author | : David Marco |
| Publisher | : John Wiley & Sons |
| Total Pages | : 482 |
| Release | : 2004-03-25 |
| Genre | : Computers |
| ISBN | : 0764571591 |
* The heart of the book provides the complete set of models that will support most of an organization's core business functions, including universal meta models for enterprise-wide systems, business meta data and data stewardship, portfolio management, business rules, and XML, messaging, and transactions * Developers can directly adapt these models to their own businesses, saving countless hours of development time * Building effective meta data repositories is complicated and time-consuming, and few IT departments have the necessary expertise to do it right-which is why this book is sure to find a ready audience * Begins with a quick overview of the Meta Data Repository Environment and the business uses of meta data, then goes on to describe the technical architecture followed by the detailed models
| Author | : Len Silverston |
| Publisher | : John Wiley & Sons |
| Total Pages | : 650 |
| Release | : 2011-03-21 |
| Genre | : Computers |
| ISBN | : 1118080831 |
This third volume of the best-selling "Data Model Resource Book" series revolutionizes the data modeling discipline by answering the question "How can you save significant time while improving the quality of any type of data modeling effort?" In contrast to the first two volumes, this new volume focuses on the fundamental, underlying patterns that affect over 50 percent of most data modeling efforts. These patterns can be used to considerably reduce modeling time and cost, to jump-start data modeling efforts, as standards and guidelines to increase data model consistency and quality, and as an objective source against which an enterprise can evaluate data models.
| Author | : A. Stein |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 232 |
| Release | : 2013-04-17 |
| Genre | : Technology & Engineering |
| ISBN | : 9401592179 |
Under leadership of CT de Wit a large amount of modeling, building prototypes and also application, was carried out in the 1970s and 1980s. Comprehensive models were built, evaluated and carefully documented in the areas of crop growth production, plant breeding, soil water and nutrients, and in crop protection. Simulation techniques and biophysical theories developed in parallel. Simulation and experimentation always went hand in hand. Much of this work is documented in a long series of PhD theses under supervision of De Wit, in the series of Simulation Monographs (PUDOC), and in numerous other publications. This work has inspired many scientists across the global science community. The CT de Wit Graduate School of Production Ecology (PE) of the Wageningen University builds further on this platform and finds new subjects for research on and with models, and data. The PE platform provides also an excellent opportunity to develop contacts, cooperation and joint software with research groups in related fields and abroad. This book precipitates from such an exploration in new directions. We realize that modem information systems and statistics can offer a substantial contribution to the modelling framework. Good examples can be found here, and these provide a clear direction for the years to come.
