Books in Data

Smart Spaces

1st Edition
March 18, 2024
Fatos Xhafa + 1 more
English
Paperback
9 7 8 0 4 4 3 1 3 4 6 2 3
eBook
9 7 8 0 4 4 3 1 3 4 6 3 0

Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems.People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks – a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

Object-Oriented Analysis and Design for Information Systems

2nd Edition
March 16, 2024
Raul Sidnei Wazlawick
English
Paperback
9 7 8 0 4 4 3 1 3 7 3 9 6
eBook
9 7 8 0 4 4 3 1 3 7 4 0 2

Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

Synthetic Data and Generative AI

1st Edition
January 9, 2024
Vincent Granville
English
Paperback
9 7 8 0 4 4 3 2 1 8 5 7 6
eBook
9 7 8 0 4 4 3 2 1 8 5 6 9

Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques – including logistic and Lasso – are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.

Mathematical Methods in Data Science

1st Edition
January 6, 2023
Jingli Ren + 1 more
English
Paperback
9 7 8 0 4 4 3 1 8 6 7 9 0
eBook
9 7 8 0 4 4 3 1 8 6 8 0 6

Mathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors’ recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and the clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.

Data Analytics for Social Microblogging Platforms

1st Edition
November 4, 2022
Soumi Dutta + 3 more
English
Paperback
9 7 8 0 3 2 3 9 1 7 8 5 8
eBook
9 7 8 0 3 2 3 9 7 2 3 0 7

Data Analysis for Social Microblogging Platforms explores the nature of microblog datasets, also covering the larger field which focuses on information, data and knowledge in the context of natural language processing. The book investigates a range of significant computational techniques which enable data and computer scientists to recognize patterns in these vast datasets, including machine learning, data mining algorithms, rough set and fuzzy set theory, evolutionary computations, combinatorial pattern matching, clustering, summarization and classification. Chapters focus on basic online micro blogging data analysis research methodologies, community detection, summarization application development, performance evaluation and their applications in big data.

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

Now taught in a majority of medical schools nationwide, health systems science (HSS) prepares learners for the health systems of the future—an essential topic in modern health care. Health Systems Science Education, part of the American Medical Association’s MedEd Innovation Series, is a first-of-its-kind, instructor-focused field book that that equips educators to not just teach health systems science, but to know how to integrate and implement HSS comprehensively and effectively across the curriculum. This change management-oriented volume.

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

Health Systems Science Education: Development and Implementation

1st Edition
Volume 4
September 9, 2022
Rosalyn Maben-Feaster + 5 more
English

Meeting the Challenges of Data Quality Management

1st Edition
January 25, 2022
Laura Sebastian-Coleman
English
Paperback
9 7 8 0 1 2 8 2 1 7 3 7 5
eBook
9 7 8 0 1 2 8 2 1 7 5 6 6

Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses.

Related subjects

Coding and information theory

Data encryption

Data general

Data storage representations

Data structures

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Books in Data

Smart Spaces

Object-Oriented Analysis and Design for Information Systems

Synthetic Data and Generative AI

Mathematical Methods in Data Science

Data Analytics for Social Microblogging Platforms

Health Systems Science Education: Development and Implementation

Health Systems Science Education: Development and Implementation

Health Systems Science Education: Development and Implementation

Health Systems Science Education: Development and Implementation

Meeting the Challenges of Data Quality Management

Related subjects