Author: Keith Gordon
Publisher: BCS, The Chartered Institute for IT
ISBN: 9781780171845
Category : Business & Economics
Languages : en
Pages : 250
Book Description
Data is a valuable corporate asset and its effective management can be vital to an organisation’s success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This new edition covers web technology and its relation to databases and includes material on the management of master data.
Principles of Data Management
Author: Keith Gordon
Publisher: BCS, The Chartered Institute for IT
ISBN: 9781780171845
Category : Business & Economics
Languages : en
Pages : 250
Book Description
Data is a valuable corporate asset and its effective management can be vital to an organisation’s success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This new edition covers web technology and its relation to databases and includes material on the management of master data.
Publisher: BCS, The Chartered Institute for IT
ISBN: 9781780171845
Category : Business & Economics
Languages : en
Pages : 250
Book Description
Data is a valuable corporate asset and its effective management can be vital to an organisation’s success. This professional guide covers all the key areas of data management, including database development and corporate data modelling. It is business-focused, providing the knowledge and techniques required to successfully implement the data management function. This new edition covers web technology and its relation to databases and includes material on the management of master data.
The Principle of Purpose Limitation in Data Protection Laws
Author: Maximilian von Grafenstein
Publisher: Nomos Verlagsgesellschaft
ISBN: 9783848748976
Category : Data Protection Law
Languages : en
Pages : 0
Book Description
This thesis examines the principle of purpose limitation in data protection law from the perspective of regulating data-driven innovation. According to this approach, the principle of purpose limitation not only protects an individual's autonomy but simultaneously leaves sufficient room for data controllers to innovate when finding the best solution for protection. The first component of the principle of purpose limitation (i.e. to specify the purpose of data processing) is a precautionary protection instrument which obliges the controller to identify specific risks arising from its processing against all fundamental rights of the data subject. In contrast, the second component (i.e. the requirement to limit data processing to the preceding purpose) aims to control the risk caused by data processing that occurred at a later stage and adds to the risks which were previously identified. This approach provides an answer to the question of how the General Data Protection Regulation which does not only effectively protect an individual's autonomy but also helps controllers to turn their legal compliance into a mechanism that enhances innovation, should be interpreted with regard to all the fundamental rights of the data subject.
Publisher: Nomos Verlagsgesellschaft
ISBN: 9783848748976
Category : Data Protection Law
Languages : en
Pages : 0
Book Description
This thesis examines the principle of purpose limitation in data protection law from the perspective of regulating data-driven innovation. According to this approach, the principle of purpose limitation not only protects an individual's autonomy but simultaneously leaves sufficient room for data controllers to innovate when finding the best solution for protection. The first component of the principle of purpose limitation (i.e. to specify the purpose of data processing) is a precautionary protection instrument which obliges the controller to identify specific risks arising from its processing against all fundamental rights of the data subject. In contrast, the second component (i.e. the requirement to limit data processing to the preceding purpose) aims to control the risk caused by data processing that occurred at a later stage and adds to the risks which were previously identified. This approach provides an answer to the question of how the General Data Protection Regulation which does not only effectively protect an individual's autonomy but also helps controllers to turn their legal compliance into a mechanism that enhances innovation, should be interpreted with regard to all the fundamental rights of the data subject.
Principles of Transaction Processing
Author: Philip A. Bernstein
Publisher: Morgan Kaufmann
ISBN: 0080948413
Category : Computers
Languages : en
Pages : 397
Book Description
Principles of Transaction Processing is a comprehensive guide to developing applications, designing systems, and evaluating engineering products. The book provides detailed discussions of the internal workings of transaction processing systems, and it discusses how these systems work and how best to utilize them. It covers the architecture of Web Application Servers and transactional communication paradigms.The book is divided into 11 chapters, which cover the following: Overview of transaction processing application and system structureSoftware abstractions found in transaction processing systemsArchitecture of multitier applications and the functions of transactional middleware and database serversQueued transaction processing and its internals, with IBM's Websphere MQ and Oracle's Stream AQ as examplesBusiness process management and its mechanismsDescription of the two-phase locking function, B-tree locking and multigranularity locking used in SQL database systems and nested transaction lockingSystem recovery and its failuresTwo-phase commit protocolComparison between the tradeoffs of replicating servers versus replication resourcesTransactional middleware products and standardsFuture trends, such as cloud computing platforms, composing scalable systems using distributed computing components, the use of flash storage to replace disks and data streams from sensor devices as a source of transaction requests. The text meets the needs of systems professionals, such as IT application programmers who construct TP applications, application analysts, and product developers. The book will also be invaluable to students and novices in application programming. - Complete revision of the classic "non mathematical" transaction processing reference for systems professionals - Updated to focus on the needs of transaction processing via the Internet-- the main focus of business data processing investments, via web application servers, SOA, and important new TP standards - Retains the practical, non-mathematical, but thorough conceptual basis of the first edition
Publisher: Morgan Kaufmann
ISBN: 0080948413
Category : Computers
Languages : en
Pages : 397
Book Description
Principles of Transaction Processing is a comprehensive guide to developing applications, designing systems, and evaluating engineering products. The book provides detailed discussions of the internal workings of transaction processing systems, and it discusses how these systems work and how best to utilize them. It covers the architecture of Web Application Servers and transactional communication paradigms.The book is divided into 11 chapters, which cover the following: Overview of transaction processing application and system structureSoftware abstractions found in transaction processing systemsArchitecture of multitier applications and the functions of transactional middleware and database serversQueued transaction processing and its internals, with IBM's Websphere MQ and Oracle's Stream AQ as examplesBusiness process management and its mechanismsDescription of the two-phase locking function, B-tree locking and multigranularity locking used in SQL database systems and nested transaction lockingSystem recovery and its failuresTwo-phase commit protocolComparison between the tradeoffs of replicating servers versus replication resourcesTransactional middleware products and standardsFuture trends, such as cloud computing platforms, composing scalable systems using distributed computing components, the use of flash storage to replace disks and data streams from sensor devices as a source of transaction requests. The text meets the needs of systems professionals, such as IT application programmers who construct TP applications, application analysts, and product developers. The book will also be invaluable to students and novices in application programming. - Complete revision of the classic "non mathematical" transaction processing reference for systems professionals - Updated to focus on the needs of transaction processing via the Internet-- the main focus of business data processing investments, via web application servers, SOA, and important new TP standards - Retains the practical, non-mathematical, but thorough conceptual basis of the first edition
Data Processing Handbook for Complex Biological Data Sources
Author: Gauri Misra
Publisher: Academic Press
ISBN: 0128172800
Category : Science
Languages : en
Pages : 191
Book Description
Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. - Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level - Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data - Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing
Publisher: Academic Press
ISBN: 0128172800
Category : Science
Languages : en
Pages : 191
Book Description
Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. - Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level - Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data - Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing
Principles of Data Integration
Author: AnHai Doan
Publisher: Elsevier
ISBN: 0123914795
Category : Computers
Languages : en
Pages : 522
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
Publisher: Elsevier
ISBN: 0123914795
Category : Computers
Languages : en
Pages : 522
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
Principles of Database Management
Author: Wilfried Lemahieu
Publisher: Cambridge University Press
ISBN: 1107186129
Category : Computers
Languages : en
Pages : 817
Book Description
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
Publisher: Cambridge University Press
ISBN: 1107186129
Category : Computers
Languages : en
Pages : 817
Book Description
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
GDPR: Personal Data Protection in the European Union
Author: Mariusz Krzysztofek
Publisher: Kluwer Law International B.V.
ISBN: 9403532718
Category : Law
Languages : en
Pages : 330
Book Description
GDPR: Personal Data Protection in the European Union Mariusz Krzysztofek Personal data protection has become one of the central issues in any understanding of the current world system. In this connection, the European Union (EU) has created the most sophisticated regime currently in force with the General Data Protection Regulation (GDPR) (EU) 2016/679. Following the GDPR’s recent reform – the most extensive since the first EU laws in this area were adopted and implemented into the legal orders of the Member States – this book offers a comprehensive discussion of all principles of personal data processing, obligations of data controllers, and rights of data subjects, providing a thorough, up-to-date account of the legal and practical aspects of personal data protection in the EU. Coverage includes the recent Court of Justice of the European Union (CJEU) judgment on data transfers and new or updated data protection authorities’ guidelines in the EU Member States. Among the broad spectrum of aspects of the subject covered are the following: – right to privacy judgments of the CJEU and the European Court of Human Rights; – scope of the GDPR and its key definitions, key principles of personal data processing; – legal bases for the processing of personal data; – direct and digital marketing, cookies, and online behavioural advertising; – processing of personal data of employees; – sensitive data and criminal records; – information obligation & privacy notices; – data subjects rights; – data controller, joint controllers, and processors; – data protection by design and by default, data security measures, risk-based approach, records of personal data processing activities, notification of a personal data breach to the supervisory authority and communication to the data subject, data protection impact assessment, codes of conduct and certification; – Data Protection Officer; – transfers of personal data to non-EU/EEA countries; and – privacy in the Internet and surveillance age. Because the global scale and evolution of information technologies have changed the data processing environment and brought new challenges, and because many non-EU jurisdictions have adopted equivalent regimes or largely analogous regulations, the book will be of great usefulness worldwide. Multinational corporations and their customers and contractors will benefit enormously from consulting and using this book, especially in conducting case law, guidelines and best practices formulated by European data protection authorities. For lawyers and academics researching or advising clients on this area, this book provides an indispensable source of practical guidance and information for many years to come.
Publisher: Kluwer Law International B.V.
ISBN: 9403532718
Category : Law
Languages : en
Pages : 330
Book Description
GDPR: Personal Data Protection in the European Union Mariusz Krzysztofek Personal data protection has become one of the central issues in any understanding of the current world system. In this connection, the European Union (EU) has created the most sophisticated regime currently in force with the General Data Protection Regulation (GDPR) (EU) 2016/679. Following the GDPR’s recent reform – the most extensive since the first EU laws in this area were adopted and implemented into the legal orders of the Member States – this book offers a comprehensive discussion of all principles of personal data processing, obligations of data controllers, and rights of data subjects, providing a thorough, up-to-date account of the legal and practical aspects of personal data protection in the EU. Coverage includes the recent Court of Justice of the European Union (CJEU) judgment on data transfers and new or updated data protection authorities’ guidelines in the EU Member States. Among the broad spectrum of aspects of the subject covered are the following: – right to privacy judgments of the CJEU and the European Court of Human Rights; – scope of the GDPR and its key definitions, key principles of personal data processing; – legal bases for the processing of personal data; – direct and digital marketing, cookies, and online behavioural advertising; – processing of personal data of employees; – sensitive data and criminal records; – information obligation & privacy notices; – data subjects rights; – data controller, joint controllers, and processors; – data protection by design and by default, data security measures, risk-based approach, records of personal data processing activities, notification of a personal data breach to the supervisory authority and communication to the data subject, data protection impact assessment, codes of conduct and certification; – Data Protection Officer; – transfers of personal data to non-EU/EEA countries; and – privacy in the Internet and surveillance age. Because the global scale and evolution of information technologies have changed the data processing environment and brought new challenges, and because many non-EU jurisdictions have adopted equivalent regimes or largely analogous regulations, the book will be of great usefulness worldwide. Multinational corporations and their customers and contractors will benefit enormously from consulting and using this book, especially in conducting case law, guidelines and best practices formulated by European data protection authorities. For lawyers and academics researching or advising clients on this area, this book provides an indispensable source of practical guidance and information for many years to come.
Big Data
Author: James Warren
Publisher: Simon and Schuster
ISBN: 1638351104
Category : Computers
Languages : en
Pages : 481
Book Description
Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth
Publisher: Simon and Schuster
ISBN: 1638351104
Category : Computers
Languages : en
Pages : 481
Book Description
Summary Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data. It describes a scalable, easy-to-understand approach to big data systems that can be built and run by a small team. Following a realistic example, this book guides readers through the theory of big data systems, how to implement them in practice, and how to deploy and operate them once they're built. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Web-scale applications like social networks, real-time analytics, or e-commerce sites deal with a lot of data, whose volume and velocity exceed the limits of traditional database systems. These applications require architectures built around clusters of machines to store and process data of any size, or speed. Fortunately, scale and simplicity are not mutually exclusive. Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases. This book requires no previous exposure to large-scale data analysis or NoSQL tools. Familiarity with traditional databases is helpful. What's Inside Introduction to big data systems Real-time processing of web-scale data Tools like Hadoop, Cassandra, and Storm Extensions to traditional database skills About the Authors Nathan Marz is the creator of Apache Storm and the originator of the Lambda Architecture for big data systems. James Warren is an analytics architect with a background in machine learning and scientific computing. Table of Contents A new paradigm for Big Data PART 1 BATCH LAYER Data model for Big Data Data model for Big Data: Illustration Data storage on the batch layer Data storage on the batch layer: Illustration Batch layer Batch layer: Illustration An example batch layer: Architecture and algorithms An example batch layer: Implementation PART 2 SERVING LAYER Serving layer Serving layer: Illustration PART 3 SPEED LAYER Realtime views Realtime views: Illustration Queuing and stream processing Queuing and stream processing: Illustration Micro-batch stream processing Micro-batch stream processing: Illustration Lambda Architecture in depth
Fundamentals of Clinical Data Science
Author: Pieter Kubben
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219
Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Publisher: Springer
ISBN: 3319997130
Category : Medical
Languages : en
Pages : 219
Book Description
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Principles of Data Mining
Author: Max Bramer
Publisher: Springer
ISBN: 1447173074
Category : Computers
Languages : en
Pages : 530
Book Description
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.
Publisher: Springer
ISBN: 1447173074
Category : Computers
Languages : en
Pages : 530
Book Description
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.