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Author: AnHai Doan
Publisher: Elsevier
ISBN: 0123914795
Category : Computers
Languages : en
Pages : 522
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Book Description
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications
Author: AnHai Doan
Publisher: Elsevier
ISBN: 0123914795
Category : Computers
Languages : en
Pages : 522
Get Book Here
Book Description
Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application. Written by three of the most respected experts in the field, this book provides an extensive introduction to the theory and concepts underlying today's data integration techniques, with detailed, instruction for their application using concrete examples throughout to explain the concepts. This text is an ideal resource for database practitioners in industry, including data warehouse engineers, database system designers, data architects/enterprise architects, database researchers, statisticians, and data analysts; students in data analytics and knowledge discovery; and other data professionals working at the R&D and implementation levels. - Offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand - Enables you to build your own algorithms and implement your own data integration applications
Author: Wilfried Lemahieu
Publisher: Cambridge University Press
ISBN: 1107186129
Category : Computers
Languages : en
Pages : 817
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Book Description
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Author: Jules J. Berman
Publisher: Newnes
ISBN: 0124047246
Category : Computers
Languages : en
Pages : 288
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Book Description
Principles of Big Data helps readers avoid the common mistakes that endanger all Big Data projects. By stressing simple, fundamental concepts, this book teaches readers how to organize large volumes of complex data, and how to achieve data permanence when the content of the data is constantly changing. General methods for data verification and validation, as specifically applied to Big Data resources, are stressed throughout the book. The book demonstrates how adept analysts can find relationships among data objects held in disparate Big Data resources, when the data objects are endowed with semantic support (i.e., organized in classes of uniquely identified data objects). Readers will learn how their data can be integrated with data from other resources, and how the data extracted from Big Data resources can be used for purposes beyond those imagined by the data creators. - Learn general methods for specifying Big Data in a way that is understandable to humans and to computers - Avoid the pitfalls in Big Data design and analysis - Understand how to create and use Big Data safely and responsibly with a set of laws, regulations and ethical standards that apply to the acquisition, distribution and integration of Big Data resources
Author: Tye Rattenbury
Publisher: "O'Reilly Media, Inc."
ISBN: 1491938870
Category : Computers
Languages : en
Pages : 117
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Book Description
A key task that any aspiring data-driven organization needs to learn is data wrangling, the process of converting raw data into something truly useful. This practical guide provides business analysts with an overview of various data wrangling techniques and tools, and puts the practice of data wrangling into context by asking, "What are you trying to do and why?" Wrangling data consumes roughly 50-80% of an analyst’s time before any kind of analysis is possible. Written by key executives at Trifacta, this book walks you through the wrangling process by exploring several factors—time, granularity, scope, and structure—that you need to consider as you begin to work with data. You’ll learn a shared language and a comprehensive understanding of data wrangling, with an emphasis on recent agile analytic processes used by many of today’s data-driven organizations. Appreciate the importance—and the satisfaction—of wrangling data the right way. Understand what kind of data is available Choose which data to use and at what level of detail Meaningfully combine multiple sources of data Decide how to distill the results to a size and shape that can drive downstream analysis
Author: M. Tamer Özsu
Publisher: Springer Science & Business Media
ISBN: 1441988343
Category : Computers
Languages : en
Pages : 856
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Book Description
This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: • New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. • Coverage of emerging topics such as data streams and cloud computing • Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.
Author: Carlo Batini
Publisher: Springer
ISBN: 3319241060
Category : Computers
Languages : en
Pages : 520
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Book Description
This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.
Author: Dave Armes
Publisher: Van Haren
ISBN: 9401805784
Category : Architecture
Languages : en
Pages : 225
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Book Description
For trainers free additional material of this book is available. This can be found under the "Training Material" tab. Log in with your trainer account to access the material. The increasing complexity of the IT value chain and the rise of multi-vendor supplier ecosystems has led to the rise of Service Integration and Management (SIAM) as a new approach. Service Integration is the set of principles and practices, which facilitate the collaborative working relationships between service providers required to maximize the benefit of multi-sourcing. Service integration facilitates the linkage of services, the technology of which they are comprised and the delivery organizations and processes used to operate them, into a single operating model. SIAM is a relatively new and fast evolving concept. SIAM teams are being established in many organizations and in many different sectors, as part of a strategy for (out)sourcing IT services and other types of service. This is the first book that describes the concepts of SIAM. It is intended for: ITSM professionals working in integrated multi-sourced environments; Service customer managers, with a responsibility to secure the business supply of IT services in a multi-sourced environment; Service provider delivery managers with a responsibility to integrate multiple services to meet the demands of the customers business and users; Service provider managers with responsibilities to manage integrated services, participating in a multi-sourced environment.
Author: Anne Laurent
Publisher: John Wiley & Sons
ISBN: 1786305852
Category : Computers
Languages : en
Pages : 244
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Book Description
The concept of a data lake is less than 10 years old, but they are already hugely implemented within large companies. Their goal is to efficiently deal with ever-growing volumes of heterogeneous data, while also facing various sophisticated user needs. However, defining and building a data lake is still a challenge, as no consensus has been reached so far. Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of a data lake; the management of metadata supplying key information about the stored data, master data and reference data; the roles of linked data and fog computing in a data lake ecosystem; and how gravity principles apply in the context of data lakes. A variety of case studies are also presented, thus providing the reader with practical examples of data lake management.
Author: Tomasz Wiktorski
Publisher: Springer
ISBN: 3030046036
Category : Computers
Languages : en
Pages : 105
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Book Description
Data-intensive systems are a technological building block supporting Big Data and Data Science applications.This book familiarizes readers with core concepts that they should be aware of before continuing with independent work and the more advanced technical reference literature that dominates the current landscape. The material in the book is structured following a problem-based approach. This means that the content in the chapters is focused on developing solutions to simplified, but still realistic problems using data-intensive technologies and approaches. The reader follows one reference scenario through the whole book, that uses an open Apache dataset. The origins of this volume are in lectures from a master’s course in Data-intensive Systems, given at the University of Stavanger. Some chapters were also a base for guest lectures at Purdue University and Lodz University of Technology.
Author: Rick van der Lans
Publisher: Elsevier
ISBN: 0123944252
Category : Business & Economics
Languages : en
Pages : 297
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Book Description
Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects.
