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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
Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science.
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.
The phenomenon of Extended X-Ray Absorption Fine Structure (EXAFS) has been known for some time and was first treated theoretically by Kronig in the 1930s. Recent developments, initiated by Sayers, Stern, and Lytle in the early 1970s, have led to the recognition of the structural content of this technique. At the same time, the availability of synchrotron radiation has greatly improved both the acquisition and the quality of the EXAFS data over those obtainable from conventional X-ray sources. Such developments have established EXAFS as a powerful tool for structure studies. EXAFS has been successfully applied to a wide range of significant scientific and technological systems in many diverse fields such as inorganic chemistry, biochemistry, catalysis, material sciences, etc. It is extremely useful for systems where single-crystal diffraction techniques are not readily applicable (e.g., gas, liquid, solution, amorphous and polycrystalline solids, surfaces, polymer, etc.). Despite the fact that the EXAFS technique and applications have matured tremendously over the past decade or so, no introductory textbook exists. EXAFS: Basic Principles and Data Analysis represents my modest attempt to fill such a gap. In this book, I aim to introduce the subject matter to the novice and to help alleviate the confusion in EXAFS data analysis, which, although becoming more and more routine, is still a rather tricky endeavor and may, at times, discourage the beginners.
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 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
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
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
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.
Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.