Big Data Analytics (Database Management Systems) PDF Download
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Author: Conor Suarez
Publisher:
ISBN: 9781682851807
Category : Big data
Languages : en
Pages : 0
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Book Description
Big data is a broad term that defines the structured or unstructured data sets. It is a challenging field which includes analysis, transfer and visualization of data. This book presents in-depth description of various dimensions of big data analysis like complexity and variability. It elucidates the handling and storing of data through database management systems. It examines the challenges involved in big data analysis including data mining tools and techniques. Students and professionals engaged in this field will find this book beneficial.
Author: Conor Suarez
Publisher:
ISBN: 9781682851807
Category : Big data
Languages : en
Pages : 0
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Book Description
Big data is a broad term that defines the structured or unstructured data sets. It is a challenging field which includes analysis, transfer and visualization of data. This book presents in-depth description of various dimensions of big data analysis like complexity and variability. It elucidates the handling and storing of data through database management systems. It examines the challenges involved in big data analysis including data mining tools and techniques. Students and professionals engaged in this field will find this book beneficial.
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: Nicholas J. Horton
Publisher: CRC Press
ISBN: 1439827567
Category : Mathematics
Languages : en
Pages : 299
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Book Description
Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsUsing R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes
Author: Joe Celko
Publisher: Elsevier
ISBN: 0080495931
Category : Computers
Languages : en
Pages : 205
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Book Description
Joe Celko's Analytics and OLAP in SQL is the first book that teaches what SQL programmers need in order to successfully make the transition from On-Line Transaction Processing (OLTP) systems into the world of On-Line Analytical Processing (OLAP). This book is not an in-depth look at particular subjects, but an overview of many subjects that will give the working RDBMS programmers a map of the terra incognita they will face — if they want to grow. It contains expert advice from a noted SQL authority and award-winning columnist, who has given ten years of service to the ANSI SQL standards committee and many more years of dependable help to readers of online forums. It offers real-world insights and lots of practical examples. It covers the OLAP extensions in SQL-99; ETL tools, OLAP features supported in DBMSs, other query tools, simple reports, and statistical software. This book is ideal for experienced SQL programmers who have worked with OLTP systems who need to learn techniques—and even some tricks—that they can use in an OLAP situation. Expert advice from a noted SQL authority and award-winning columnist, who has given ten years of service to the ANSI SQL standards committee and many more years of dependable help to readers of online forums First book that teaches what SQL programmers need in order to successfully make the transition from transactional systems (OLTP) into the world of data warehouse data and OLAP Offers real-world insights and lots of practical examples Covers the OLAP extensions in SQL-99; ETL tools, OLAP features supported in DBMSs, other query tools, simple reports, and statistical software
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1799812057
Category : Medical
Languages : en
Pages : 2071
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Book Description
Advancements in data science have created opportunities to sort, manage, and analyze large amounts of data more effectively and efficiently. Applying these new technologies to the healthcare industry, which has vast quantities of patient and medical data and is increasingly becoming more data-reliant, is crucial for refining medical practices and patient care. Data Analytics in Medicine: Concepts, Methodologies, Tools, and Applications is a vital reference source that examines practical applications of healthcare analytics for improved patient care, resource allocation, and medical performance, as well as for diagnosing, predicting, and identifying at-risk populations. Highlighting a range of topics such as data security and privacy, health informatics, and predictive analytics, this multi-volume book is ideally designed for doctors, hospital administrators, nurses, medical professionals, IT specialists, computer engineers, information technologists, biomedical engineers, data-processing specialists, healthcare practitioners, academicians, and researchers interested in current research on the connections between data analytics in the field of medicine.
Author: Dama International
Publisher:
ISBN: 9781634622349
Category : Database management
Languages : en
Pages : 628
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Book Description
Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment.
Author: ChengXiang Zhai
Publisher: Morgan & Claypool
ISBN: 1970001186
Category : Computers
Languages : en
Pages : 634
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Book Description
Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic. This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.
Author: Stanisław Kozielski
Publisher: Springer Science & Business Media
ISBN: 9780387874302
Category : Business & Economics
Languages : en
Pages : 365
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Book Description
Most of modern enterprises, institutions, and organizations rely on knowledge-based management systems. In these systems, knowledge is gained from data analysis. Today, knowledge-based management systems include data warehouses as their core components. Data integrated in a data warehouse are analyzed by the so-called On-Line Analytical Processing (OLAP) applications designed to discover trends, patterns of behavior, and anomalies as well as finding dependencies between data. Massive amounts of integrated data and the complexity of integrated data coming from many different sources make data integration and processing challenging. New Trends in Data Warehousing and Data Analysis brings together the most recent research and practical achievements in the DW and OLAP technologies. It provides an up-to-date bibliography of published works and the resource of research achievements. Finally, the book assists in the dissemination of knowledge in the field of advanced DW and OLAP.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309287812
Category : Mathematics
Languages : en
Pages : 191
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Book Description
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: Ivan Damnjanovic
Publisher: Springer
ISBN: 3030142515
Category : Technology & Engineering
Languages : en
Pages : 379
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Book Description
This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.
