Skip to main content
Elsevier

Books in Database management

  • Ethics of Blockchain by Design

    • 1st Edition
    • Muthu Ramachandran
    • English
    The increasing integration of blockchain into critical sectors such as finance, healthcare, and governance has prompted a pressing need to address ethical considerations from the ground up. While blockchain offers revolutionary potential for transparency, decentralization, and trust, it also introduces profound challenges around accountability, privacy, inclusion, and governance. Current literature often focuses on the technological and economic dimensions of blockchain but overlooks its ethical implications, leaving a critical gap in both research and practice. Ethics of Blockchain by Design addresses this gap by offering a comprehensive, design-centred framework that integrates ethical principles into every stage of blockchain system development. Rooted in systems engineering, the book systematically explores how ethical requirements can be specified, implemented, and validated within decentralized technologies. It goes beyond theoretical discussions by providing actionable methodologies, case studies, governance models, and compliance tools, making it essential reading for blockchain architects, developers, students, and policy makers. This book positions itself at the intersection of engineering, ethics, and decentralized innovation, and builds on the author’s extensive contributions to AI ethics, secure software systems, and blockchain in healthcare. As the demand for responsible digital infrastructure grows, Ethics of Blockchain by Design offers the first structured guide to ensuring blockchain technologies are not only technically sound but also ethically robust and socially accountable.

  • Mastering Cloud Computing

    Foundations and Applications Programming
    • 2nd Edition
    • Rajkumar Buyya + 4 more
    • English
    Mastering Cloud Computing: Foundations and Applications Programming, Second Edition serves as a comprehensive introduction for readers seeking to develop applications in the ever-evolving world of cloud computing. As technology advances, applications are no longer confined to a single machine but instead operate from virtual servers, accessible globally at any time. This book equips aspiring developers with the essential tools and knowledge to create effective cloud-based applications. Beyond the foundational principles, the book delves into distributed and parallel computing, providing in-depth coverage of virtualization, thread programming, task programming, and map-reduce techniques.It also addresses the development of applications for various cloud architectures, highlighting industrial platforms and critical security considerations. To reinforce learning, the text integrates real-world case studies, practical examples, hands-on exercises, and lab activities throughout, allowing readers to apply concepts directly and build their expertise effectively.

  • Mathematical Modeling for Big Data Analytics

    • 1st Edition
    • Passent El-Kafrawy + 1 more
    • English
    Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.

  • The Essential Criteria of Graph Databases

    • 1st Edition
    • Ricky Sun
    • English
    The Essential Criteria of Graph Databases collects several truly innovative graph applications in asset-liability and liquidity risk management to spark readers’ interest and further broaden the reach and applicable domains of graph systems. Although AI has incredible potential, it has three weak links: 1. Blackbox, lack of explainability, 2. Silos, slews of siloed systems across the AI ecosystem, 3. Low-performance, as most of ML/DL based AI systems are SLOW. Hence, fixing these problems paves the road to strong and effective AI.

  • Machine Learning

    A Constraint-Based Approach
    • 2nd Edition
    • Marco Gori + 2 more
    • English
    Machine Learning: A Constraint-Based Approach, Second Edition provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that include neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. It draws a path towards deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, such as in fuzzy systems. Special attention is given to deep learning, which nicely fits the constrained-based approach followed in this book.The book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, including many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.

  • Classification Made Relevant

    How Scientists Build and Use Classifications and Ontologies
    • 1st Edition
    • Jules J. Berman
    • English
    Classification Made Relevant: How Scientists Build and Use Classifications and Ontologies explains how classifications and ontologies are designed and used to analyze scientific information. The book presents the fundamentals of classification, leading up to a description of how computer scientists use object-oriented programming languages to model classifications and ontologies. Numerous examples are chosen from the Classification of Life, the Periodic Table of the Elements, and the symmetry relationships contained within the Classification Theorem of Finite Simple Groups. When these three classifications are tied together, they provide a relational hierarchy connecting all of the natural sciences. The book's chapters introduce and describe general concepts that can be understood by any intelligent reader. With each new concept, they follow practical examples selected from various scientific disciplines. In these cases, technical points and specialized vocabulary are linked to glossary items where the item is clarified and expanded.

  • Data Stewardship

    An Actionable Guide to Effective Data Management and Data Governance
    • 2nd Edition
    • David Plotkin
    • English
    Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality. Since the publication of the first edition, there have been critical new developments in the field, such as integrating Data Stewardship into project management, handling Data Stewardship in large international companies, handling "big data" and Data Lakes, and a pivot in the overall thinking around the best way to align data stewardship to the data—moving from business/organizatio... function to data domain. Furthermore, the role of process in data stewardship is now recognized as key and needed to be covered.Data Stewardship, Second Edition provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/compa... structure, business functions, and data ownership. The book shows data managers how to gain support for a stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort. It includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards.

  • Data Architecture: A Primer for the Data Scientist

    A Primer for the Data Scientist
    • 2nd Edition
    • W.H. Inmon + 2 more
    • English
    Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.

  • Big Data Analytics for Sensor-Network Collected Intelligence

    • 1st Edition
    • Hui-Huang Hsu + 2 more
    • English
    Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS

  • Temporal Data Mining via Unsupervised Ensemble Learning

    • 1st Edition
    • Yun Yang
    • English
    Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics.

  • The Data and Analytics Playbook

    Proven Methods for Governed Data and Analytic Quality
    • 1st Edition
    • Lowell Fryman + 2 more
    • English
    The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization.

  • e-Health Systems

    Theory and Technical Applications
    • 1st Edition
    • Joel J.P.C. Rodrigues + 2 more
    • English
    e-Health Systems: Theory, Advances and Technical Applications offers a global vision of all the parties involved with e-health system deployment and its operation process, presenting the state of the art in major trends for improving healthcare quality and efficiency of healthcare management. The authors focus on ICT technologies and solutions for health management and healthcare applications, specifically emerging ICT to help reduce costs and improve healthcare quality, and healthcare trends in consumer empowerment and information-rich "Smart Care", with ubiquitous care access from anywhere, at any time, by any authorized person(s) when needed. Split into two parts, this book provides a comprehensive introduction to the concepts of e-health and delves into the processes carried out to store information, as well as the standards that are used; the authors explore applications and implementation of e-health systems, explaining in depth the types of wireless networks and security protocols employed to convert these systems into robust solutions avoiding any kind of data corruption and vulnerabilities.

  • Perspectives on Data Science for Software Engineering

    • 1st Edition
    • Tim Menzies + 2 more
    • English
    Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

  • Formative Assessment, Learning Data Analytics and Gamification

    In ICT Education
    • 1st Edition
    • Santi Caballé + 1 more
    • English
    Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance.

  • Big Data and Ethics

    The Medical Datasphere
    • 1st Edition
    • Jérôme Béranger
    • English
    Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of this “massive data” seems to be essential.This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature.

  • Relational Database Design and Implementation

    • 4th Edition
    • Jan L. Harrington
    • English
    Relational Database Design and Implementation: Clearly Explained, Fourth Edition, provides the conceptual and practical information necessary to develop a database design and management scheme that ensures data accuracy and user satisfaction while optimizing performance. Database systems underlie the large majority of business information systems. Most of those in use today are based on the relational data model, a way of representing data and data relationships using only two-dimensional tables. This book covers relational database theory as well as providing a solid introduction to SQL, the international standard for the relational database data manipulation language. The book begins by reviewing basic concepts of databases and database design, then turns to creating, populating, and retrieving data using SQL. Topics such as the relational data model, normalization, data entities, and Codd's Rules (and why they are important) are covered clearly and concisely. In addition, the book looks at the impact of big data on relational databases and the option of using NoSQL databases for that purpose.

  • Business Intelligence Strategy and Big Data Analytics

    A General Management Perspective
    • 1st Edition
    • Steve Williams
    • English
    Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability. It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion. Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others. It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges. In recent years, terms like “big data” and “big data analytics” have been introduced into the business and technical lexicon. Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness. Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate. More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both.

  • Cloud Computing in Ocean and Atmospheric Sciences

    • 1st Edition
    • Tiffany C Vance + 3 more
    • English
    Cloud Computing in Ocean and Atmospheric Sciences provides the latest information on this relatively new platform for scientific computing, which has great possibilities and challenges, including pricing and deployments costs and applications that are often presented as primarily business oriented. In addition, scientific users may be very familiar with these types of models and applications, but relatively unfamiliar with the intricacies of the hardware platforms they use. The book provides a range of practical examples of cloud applications that are written to be accessible to practitioners, researchers, and students in affiliated fields. By providing general information on the use of the cloud for oceanographic and atmospheric computing, as well as examples of specific applications, this book encourages and educates potential users of the cloud. The chapters provide an introduction to the practical aspects of deploying in the cloud, also providing examples of workflows and techniques that can be reused in new projects.

  • The Visual Imperative

    Creating a Visual Culture of Data Discovery
    • 1st Edition
    • Lindy Ryan
    • English
    Data is powerful. It separates leaders from laggards and it drives business disruption, transformation, and reinvention. Today’s most progressive companies are using the power of data to propel their industries into new areas of innovation, specialization, and optimization. The horsepower of new tools and technologies have provided more opportunities than ever to harness, integrate, and interact with massive amounts of disparate data for business insights and value – something that will only continue in the era of the Internet of Things. And, as a new breed of tech-savvy and digitally native knowledge workers rise to the ranks of data scientist and visual analyst, the needs and demands of the people working with data are changing, too. The world of data is changing fast. And, it’s becoming more visual. Visual insights are becoming increasingly dominant in information management, and with the reinvigorated role of data visualization, this imperative is a driving force to creating a visual culture of data discovery. The traditional standards of data visualizations are making way for richer, more robust and more advanced visualizations and new ways of seeing and interacting with data. However, while data visualization is a critical tool to exploring and understanding bigger and more diverse and dynamic data, by understanding and embracing our human hardwiring for visual communication and storytelling and properly incorporating key design principles and evolving best practices, we take the next step forward to transform data visualizations from tools into unique visual information assets.

  • Digital Libraries

    • 1st Edition
    • Fabrice Papy
    • English
    The technological interoperability of digital libraries must be rethought in order to adapt to new uses and networks. Informative digital environments aimed at responding to heritage, cultural, scientific or commercial demands have taken over the global cyberspace and have redesigned the techno-informative landscape of the Web. However, while the technological models demonstrate their effectiveness and explain to a large extent the creation of digital libraries, archives and deposits, the subjacent concept of uses continues to cause debate. The information technologies used by heterogeneous digital libraries enable a technical interoperability of content. This is not enough to allow the adhesion of a public connected to very different information profiles and techniques. This book explores the avenues of a user-orientated interoperability where the questions of consultation interfaces and content description processes are studied.

  • Optimizing the Display and Interpretation of Data

    • 1st Edition
    • Robert Warner
    • English
    "What information do these data reveal?" "Is the information correct?" "How can I make the best use of the information?" The widespread use of computers and our reliance on the data generated by them have made these questions increasingly common and important. Computerized data may be in either digital or analog form and may be relevant to a wide range of applications that include medical monitoring and diagnosis, scientific research, engineering, quality control, seismology, meteorology, political and economic analysis and business and personal financial applications. The sources of the data may be databases that have been developed for specific purposes or may be of more general interest and include those that are accessible on the Internet. In addition, the data may represent either single or multiple parameters. Examining data in its initial form is often very laborious and also makes it possible to "miss the forest for the trees" by failing to notice patterns in the data that are not readily apparent. To address these problems, this monograph describes several accurate and efficient methods for displaying, reviewing and analyzing digital and analog data. The methods may be used either singly or in various combinations to maximize the value of the data to those for whom it is relevant. None of the methods requires special devices and each can be used on common platforms such as personal computers, tablets and smart phones. Also, each of the methods can be easily employed utilizing widely available off-the-shelf software. Using the methods does not require special expertise in computer science or technology, graphical design or statistical analysis. The usefulness and accuracy of all the described methods of data display, review and interpretation have been confirmed in multiple carefully performed studies using independent, objective endpoints. These studies and their results are described in the monograph. Because of their ease of use, accuracy and efficiency, the methods for displaying, reviewing and analyzing data described in this monograph can be highly useful to all who must work with computerized information and make decisions based upon it.

  • Agile Data Warehousing for the Enterprise

    A Guide for Solution Architects and Project Leaders
    • 1st Edition
    • Ralph Hughes
    • English
    Building upon his earlier book that detailed agile data warehousing programming techniques for the Scrum master, Ralph's latest work illustrates the agile interpretations of the remaining software engineering disciplines: Requirements management benefits from streamlined templates that not only define projects quickly, but ensure nothing essential is overlooked. Data engineering receives two new "hyper modeling" techniques, yielding data warehouses that can be easily adapted when requirements change without having to invest in ruinously expensive data-conversion programs. Quality assurance advances with not only a stereoscopic top-down and bottom-up planning method, but also the incorporation of the latest in automated test engines. Use this step-by-step guide to deepen your own application development skills through self-study, show your teammates the world's fastest and most reliable techniques for creating business intelligence systems, or ensure that the IT department working for you is building your next decision support system the right way.

  • Building a Scalable Data Warehouse with Data Vault 2.0

    • 1st Edition
    • Daniel Linstedt + 1 more
    • English
    The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture.

  • Data Analysis in the Cloud

    Models, Techniques and Applications
    • 1st Edition
    • Domenico Talia + 2 more
    • English
    Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.

  • Structured Search for Big Data

    From Keywords to Key-objects
    • 1st Edition
    • Mikhail Gilula
    • English
    The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information from the Big Data. As a Big Data Consultant, Mikhail Gilula combines academic background with 20 years of industry experience in the database and data warehousing technologies working as a Sr. Data Architect for Teradata, Alcatel-Lucent, and PayPal, among others. He has authored three books, including The Set Model for Database and Information Systems and holds four US Patents in Structured Search and Data Integration.

  • Securing SQL Server

    Protecting Your Database from Attackers
    • 3rd Edition
    • Denny Cherry
    • English
    SQL server is the most widely-used database platform in the world, and a large percentage of these databases are not properly secured, exposing sensitive customer and business data to attack. In Securing SQL Server, Third Edition, you will learn about the potential attack vectors that can be used to break into SQL server databases as well as how to protect databases from these attacks. In this book, Denny Cherry - a Microsoft SQL MVP and one of the biggest names in SQL server - will teach you how to properly secure an SQL server database from internal and external threats using best practices as well as specific tricks that the author employs in his role as a consultant for some of the largest SQL server deployments in the world. Fully updated to cover the latest technology in SQL Server 2014, this new edition walks you through how to secure new features of the 2014 release. New topics in the book include vLANs, setting up RRAS, anti-virus installs, key management, moving from plaintext to encrypted values in an existing application, securing Analysis Services Objects, Managed Service Accounts, OS rights needed by the DBA, SQL Agent Security, Table Permissions, Views, Stored Procedures, Functions, Service Broker Objects, and much more.

  • Multi-Domain Master Data Management

    Advanced MDM and Data Governance in Practice
    • 1st Edition
    • Mark Allen + 1 more
    • English
    Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration.

  • CMDB Systems

    Making Change Work in the Age of Cloud and Agile
    • 1st Edition
    • Dennis Drogseth + 2 more
    • English
    CMDB Systems: Making Change Work in the Age of Cloud and Agile shows you how an integrated database across all areas of an organization’s information system can help make organizations more efficient reduce challenges during change management and reduce total cost of ownership (TCO). In addition, this valuable reference provides guidelines that will enable you to avoid the pitfalls that cause CMDB projects to fail and actually shorten the time required to achieve an implementation of a CMDB. Drawing upon extensive experience and using illustrative real world examples, Rick Sturm, Dennis Drogseth and Dan Twing discuss: Unique insights from extensive industry exposure, research and consulting on the evolution of CMDB/CMS technology and ongoing dialog with the vendor community in terms of current and future CMDB/CMS design and plans Proven and structured best practices for CMDB deployments Clear and documented insights into the impacts of cloud computing and other advances on CMDB/CMS futures

  • Reliability Assurance of Big Data in the Cloud

    Cost-Effective Replication-Based Storage
    • 1st Edition
    • Yun Yang + 2 more
    • English
    With the rapid growth of Cloud computing, the size of Cloud data is expanding at a dramatic speed. A huge amount of data is generated and processed by Cloud applications, putting a higher demand on cloud storage. While data reliability should already be a requirement, data in the Cloud needs to be stored in a highly cost-effective manner. This book focuses on the trade-off between data storage cost and data reliability assurance for big data in the Cloud. Throughout the whole Cloud data lifecycle, four major features are presented: first, a novel generic data reliability model for describing data reliability in the Cloud; second, a minimum replication calculation approach for meeting a given data reliability requirement to facilitate data creation; third, a novel cost-effective data reliability assurance mechanism for big data maintenance, which could dramatically reduce the storage space needed in the Cloud; fourth, a cost-effective strategy for facilitating data creation and recovery, which could significantly reduce the energy consumption during data transfer.

  • How to Define and Build an Effective Cyber Threat Intelligence Capability

    • 1st Edition
    • Henry Dalziel
    • Eric Olson + 1 more
    • English
    Intelligence-Led Security: How to Understand, Justify and Implement a New Approach to Security is a concise review of the concept of Intelligence-Led Security. Protecting a business, including its information and intellectual property, physical infrastructure, employees, and reputation, has become increasingly difficult. Online threats come from all sides: internal leaks and external adversaries; domestic hacktivists and overseas cybercrime syndicates; targeted threats and mass attacks. And these threats run the gamut from targeted to indiscriminate to entirely accidental. Among thought leaders and advanced organizations, the consensus is now clear. Defensive security measures: antivirus software, firewalls, and other technical controls and post-attack mitigation strategies are no longer sufficient. To adequately protect company assets and ensure business continuity, organizations must be more proactive. Increasingly, this proactive stance is being summarized by the phrase Intelligence-Led Security: the use of data to gain insight into what can happen, who is likely to be involved, how they are likely to attack and, if possible, to predict when attacks are likely to come. In this book, the authors review the current threat-scape and why it requires this new approach, offer a clarifying definition of what Cyber Threat Intelligence is, describe how to communicate its value to business, and lay out concrete steps toward implementing Intelligence-Led Security.

  • Predictive Analytics and Data Mining

    Concepts and Practice with RapidMiner
    • 1st Edition
    • Vijay Kotu + 1 more
    • English
    Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveA...

  • Data Architecture: A Primer for the Data Scientist

    Big Data, Data Warehouse and Data Vault
    • 1st Edition
    • W.H. Inmon + 1 more
    • English
    Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data

  • Social Data Analytics

    Collaboration for the Enterprise
    • 1st Edition
    • Krish Krishnan + 1 more
    • English
    Social Data Analytics is the first practical guide for professionals who want to employ social data for analytics and business intelligence (BI). This book provides a comprehensive overview of the technologies and platforms and shows you how to access and analyze the data. You'll explore the five major types of social data and learn from cases and platform examples to help you make the most of sentiment, behavioral, social graph, location, and rich media data. A four-step approach to the social BI process will help you access, evaluate, collaborate, and share social data with ease. You'll learn everything you need to know to monitor social media and get an overview of the leading vendors in a crowded space of BI applications. By the end of this book, you will be well prepared for your organization’s next social data analytics project.

  • Business Intelligence Guidebook

    From Data Integration to Analytics
    • 1st Edition
    • Rick Sherman
    • English
    Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors’ tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you’ll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success.

  • Guerrilla Analytics

    A Practical Approach to Working with Data
    • 1st Edition
    • Enda Ridge
    • English
    Doing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. In this book, you will learn about: The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting. Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny. Practice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research. Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions. Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects

  • Modern Enterprise Business Intelligence and Data Management

    A Roadmap for IT Directors, Managers, and Architects
    • 1st Edition
    • Alan Simon
    • English
    Nearly every large corporation and governmental agency is taking a fresh look at their current enterprise-scale business intelligence (BI) and data warehousing implementations at the dawn of the "Big Data Era"…and most see a critical need to revitalize their current capabilities. Whether they find the frustrating and business-impeding continuation of a long-standing "silos of data" problem, or an over-reliance on static production reports at the expense of predictive analytics and other true business intelligence capabilities, or a lack of progress in achieving the long-sought-after enterprise-wide "single version of the truth" – or all of the above – IT Directors, strategists, and architects find that they need to go back to the drawing board and produce a brand new BI/data warehousing roadmap to help move their enterprises from their current state to one where the promises of emerging technologies and a generation’s worth of best practices can finally deliver high-impact, architecturally evolvable enterprise-scale business intelligence and data warehousing. Author Alan Simon, whose BI and data warehousing experience dates back to the late 1970s and who has personally delivered or led more than thirty enterprise-wide BI/data warehousing roadmap engagements since the mid-1990s, details a comprehensive step-by-step approach to building a best practices-driven, multi-year roadmap in the quest for architecturally evolvable BI and data warehousing at the enterprise scale. Simon addresses the triad of technology, work processes, and organizational/human factors considerations in a manner that blends the visionary and the pragmatic.

  • Bitemporal Data

    Theory and Practice
    • 1st Edition
    • Tom Johnston
    • English
    Bitemporal data has always been important. But it was not until 2011 that the ISO released a SQL standard that supported it. Currently, among major DBMS vendors, Oracle, IBM and Teradata now provide at least some bitemporal functionality in their flagship products. But to use these products effectively, someone in your IT organization needs to know more than how to code bitemporal SQL statements. Perhaps, in your organization, that person is you. To correctly interpret business requests for temporal data, to correctly specify requirements to your IT development staff, and to correctly design bitemporal databases and applications, someone in your enterprise needs a deep understanding of both the theory and the practice of managing bitemporal data. Someone also needs to understand what the future may bring in the way of additional temporal functionality, so their enterprise can plan for it. Perhaps, in your organization, that person is you. This is the book that will show the do-it-yourself IT professional how to design and build bitemporal databases and how to write bitemporal transactions and queries, and will show those who will direct the use of vendor-provided bitemporal DBMSs exactly what is going on "under the covers" of that software.

  • Time and Relational Theory

    Temporal Databases in the Relational Model and SQL
    • 2nd Edition
    • C.J. Date + 2 more
    • English
    Time and Relational Theory provides an in-depth description of temporal database systems, which provide special facilities for storing, querying, and updating historical and future data. Traditionally, database management systems provide little or no special support for temporal data at all. This situation is changing because: Cheap storage enables retention of large volumes of historical data in data warehouses Users are now faced with temporal data problems, and need solutions Temporal features have recently been incorporated into the SQL standard, and vendors have begun to add temporal support to their DBMS products Based on the groundbreaking text Temporal Data & the Relational Model (Morgan Kaufmann, 2002) and new research led by the authors, Time and Relational Theory is the only book to offer a complete overview of the functionality of a temporal DBMS. Expert authors Nikos Lorentzos, Hugh Darwen, and Chris Date describe an approach to temporal database management that is firmly rooted in classical relational theory and will stand the test of time. This book covers the SQL:2011 temporal extensions in depth and identifies and discusses the temporal functionality still missing from SQL.

  • Deductive and Object-Oriented Databases

    Proceedings of the First International Conference on Deductive and Object-Oriented Databases (DOOD89) Kyoto Research Park, Kyoto, Japan, 4-6 December 1989
    • 1st Edition
    • W. Kim + 2 more
    • English
    Deductive databases and object-oriented databases are at the forefront of research in next-generation intelligent database systems.Object-orien... programming and design methodologies have great potential, promising to reduce the complexity of very large software systems in such domains as computer-aided design and manufacturing, integrated office information systems, and artificial intelligence. Object-oriented database systems will enhance the programmer/user productivity of such systems. Research into deductive databases is aimed at discovering efficient schemes to uniformly represent assertions and deductive rules, and to respond to highly expressive queries against the knowledge base of assertions and rules. This area of research is strongly interacting with Logic Programming which has developed in parallel, sharing Logic as a common basis. Recently, research has aimed at integrating the object-oriented paradigm and rule-based deduction to provide a single powerful framework for intelligent database systems.The aim of this book is to present research papers and technical discussions between researchers concerned with deductive databases, object-oriented databases, and their integration.

  • Concurrency Control in Distributed Database Systems

    • 1st Edition
    • Volume 3
    • W. Cellary + 2 more
    • English
    Distributed Database Systems (DDBS) may be defined as integrated database systems composed of autonomous local databases, geographically distributed and interconnected by a computer network.The purpose of this monograph is to present DDBS concurrency control algorithms and their related performance issues. The most recent results have been taken into consideration. A detailed analysis and selection of these results has been made so as to include those which will promote applications and progress in the field. The application of the methods and algorithms presented is not limited to DDBSs but also relates to centralized database systems and to database machines which can often be considered as particular examples of DDBSs.The first part of the book is devoted to basic definitions and models: the distributed database model, the transaction model and the syntactic and semantic concurrency control models. The second discusses concurrency control methods in monoversion DDBSs: the locking method, the timestamp ordering method, the validation method and hybrid methods. For each method the concept, the basic algorithms, a hierarchical version of the basic algorithms, and methods for avoiding performance failures are given. The third section covers concurrency control methods in multiversion DDBSs and the fourth, methods for the semantic concurrency model. The last part concerns performance issues of DDBSs.The book is intended primarily for DDBMS designers, but is also of use to those who are engaged in the design and management of databases in general, as well as in problems of distributed system management such as distributed operating systems and computer networks.

  • Interoperable Database Systems (DS-5)

    Proceedings of the IFIP WG2.6 Database Semantics Conference on Interoperable Database Systems (DS-5) Lorne, Victoria, Australia, 16-20 November, 1992
    • 1st Edition
    • Volume 25
    • D.K. Hsiao + 2 more
    • English
    The proliferation of databases within organizations have made it imperative to allow effective sharing of information from these disparate database systems. In addition, it is desirable that the individual systems must maintain a certain degree of autonomy over their data in order to continue to provide for their existing applications and to support controlled access to their information. Thus it becomes necessary to develop new techniques and build new functionality to interoperate these autonomous database systems and to integrate them into an overall information system. Research into interoperable database systems has advanced substantially over recent years in response to this need.The papers presented in this volume cover a wide spectrum of both theoretical and pragmatic issues related to the semantics of interoperable database systems. Topics covered include techniques to support the translation between database schema and between database languages; object oriented frameworks for supporting interoperability of heterogeneous databases, knowledge base integration and techniques for overcoming schematic discrepancies in interoperable databases. In addition, there are papers addressing issues of security transaction processing, data modelling and object identification in interoperable database systems. It is hoped the publication will represent a valuable collective contribution to research and development in the field for database researchers, implementors, designers, application builders and users alike.

  • Foundations of Deductive Databases and Logic Programming

    • 1st Edition
    • Jack Minker
    • English
    Foundations of Deductive Databases and Logic Programming focuses on the foundational issues concerning deductive databases and logic programming. The selection first elaborates on negation in logic programming and towards a theory of declarative knowledge. Discussions focus on model theory of stratified programs, fixed point theory of nonmonotonic operators, stratified programs, semantics for negation in terms of special classes of models, relation between closed world assumption and the completed database, negation as a failure, and closed world assumption. The book then takes a look at negation as failure using tight derivations for general logic programs, declarative semantics of logic programs with negation, and declarative semantics of deductive databases and logic programs. The publication tackles converting AND-control to OR-control by program transformation, optimizing dialog, equivalences of logic programs, unification, and logic programming and parallel complexity. Topics include parallelism and structured and unstructured data, parallel algorithms and complexity, solving equations, most general unifiers, systems of equations and inequations, equivalences of logic programs, and optimizing recursive programs. The selection is a valuable source of data for researchers interested in pursuing further studies on the foundations of deductive databases and logic programming.

  • Database

    Principles Programming Performance
    • 1st Edition
    • Patrick O'Neil
    • English
    Database: Principles Programming Performance provides an introduction to the fundamental principles of database systems. This book focuses on database programming and the relationships between principles, programming, and performance. Organized into 10 chapters, this book begins with an overview of database design principles and presents a comprehensive introduction to the concepts used by a DBA. This text then provides grounding in many abstract concepts of the relational model. Other chapters introduce SQL, describing its capabilities and covering the statements and functions of the programming language. This book provides as well an introduction to Embedded SQL and Dynamic SQL that is sufficiently detailed to enable students to immediately start writing database programs. The final chapter deals with some of the motivations for database systems spanning multiple CPUs, including client-server and distributed transactions. This book is a valuable resource for database administrators, application programmers, specialist users, and end users.

  • Pragmatic Enterprise Architecture

    Strategies to Transform Information Systems in the Era of Big Data
    • 1st Edition
    • James Luisi
    • English
    Pragmatic Enterprise Architecture is a practical hands-on instruction manual for enterprise architects. This book prepares you to better engage IT, management, and business users by equipping you with the tools and knowledge you need to address the most common enterprise architecture challenges. You will come away with a pragmatic understanding of and approach to enterprise architecture and actionable ideas to transform your enterprise. Experienced enterprise architect James V. Luisi generously shares life cycle architectures, transaction path analysis frameworks, and more so you can save time, energy, and resources on your next big project. As an enterprise architect, you must have relatable frameworks and excellent communication skills to do your job. You must actively engage and support a large enterprise involving a hundred architectural disciplines with a modest number of subject matter experts across business, information systems, control systems, and operations architecture. They must achieve their mission using the influence of ideas and business benefits expressed in simple terms so that any audience can understand what to do and why. Pragmatic Enterprise Architecture gives you the tools to accomplish your goals in less time with fewer resources.

  • Freemium Economics

    Leveraging Analytics and User Segmentation to Drive Revenue
    • 1st Edition
    • Eric Benjamin Seufert
    • English
    Freemium Economics presents a practical, instructive approach to successfully implementing the freemium model into your software products by building analytics into product design from the earliest stages of development. Your freemium product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don't fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the freemium model. Freemium Economics de-mystifies the freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow product launch.

  • Information Management

    Strategies for Gaining a Competitive Advantage with Data
    • 1st Edition
    • William McKnight
    • English
    Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes: Part 1: The importance of information management and analytics to business, and how data warehouses are used Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise.

  • Data Mining Applications with R

    • 1st Edition
    • Yanchang Zhao + 1 more
    • English
    Data Mining Applications with R is a great resource for researchers and professionals to understand the wide use of R, a free software environment for statistical computing and graphics, in solving different problems in industry. R is widely used in leveraging data mining techniques across many different industries, including government, finance, insurance, medicine, scientific research and more. This book presents 15 different real-world case studies illustrating various techniques in rapidly growing areas. It is an ideal companion for data mining researchers in academia and industry looking for ways to turn this versatile software into a powerful analytic tool. R code, Data and color figures for the book are provided at the RDataMining.com website.

  • Data Mining and Knowledge Discovery for Geoscientists

    • 1st Edition
    • Guangren Shi
    • English
    Currently there are major challenges in data mining applications in the geosciences. This is due primarily to the fact that there is a wealth of available mining data amid an absence of the knowledge and expertise necessary to analyze and accurately interpret the same data. Most geoscientists have no practical knowledge or experience using data mining techniques. For the few that do, they typically lack expertise in using data mining software and in selecting the most appropriate algorithms for a given application. This leads to a paradoxical scenario of "rich data but poor knowledge". The true solution is to apply data mining techniques in geosciences databases and to modify these techniques for practical applications. Authored by a global thought leader in data mining, Data Mining and Knowledge Discovery for Geoscientists addresses these challenges by summarizing the latest developments in geosciences data mining and arming scientists with the ability to apply key concepts to effectively analyze and interpret vast amounts of critical information.

  • Joe Celko’s Complete Guide to NoSQL

    What Every SQL Professional Needs to Know about Non-Relational Databases
    • 1st Edition
    • Joe Celko
    • English
    Joe Celko's Complete Guide to NoSQL provides a complete overview of non-relational technologies so that you can become more nimble to meet the needs of your organization. As data continues to explode and grow more complex, SQL is becoming less useful for querying data and extracting meaning. In this new world of bigger and faster data, you will need to leverage non-relational technologies to get the most out of the information you have. Learn where, when, and why the benefits of NoSQL outweigh those of SQL with Joe Celko's Complete Guide to NoSQL. This book covers three areas that make today's new data different from the data of the past: velocity, volume and variety. When information is changing faster than you can collect and query it, it simply cannot be treated the same as static data. Celko will help you understand velocity, to equip you with the tools to drink from a fire hose. Old storage and access models do not work for big data. Celko will help you understand volume, as well as different ways to store and access data such as petabytes and exabytes. Not all data can fit into a relational model, including genetic data, semantic data, and data generated by social networks. Celko will help you understand variety, as well as the alternative storage, query, and management frameworks needed by certain kinds of data.

  • Data Stewardship

    An Actionable Guide to Effective Data Management and Data Governance
    • 1st Edition
    • David Plotkin
    • English
    Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company’s data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward’s time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort.