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Books in Database management

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31-40 of 161 results in All results

Data Architecture: A Primer for the Data Scientist

  • 1st Edition
  • November 26, 2014
  • W.H. Inmon + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 2 0 4 4 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 2 0 9 1 - 3
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

RDF Database Systems

  • 1st Edition
  • November 24, 2014
  • Olivier Curé + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 7 9 9 9 5 7 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 0 4 7 0 - 8
RDF Database Systems is a cutting-edge guide that distills everything you need to know to effectively use or design an RDF database. This book starts with the basics of linked open data and covers the most recent research, practice, and technologies to help you leverage semantic technology. With an approach that combines technical detail with theoretical background, this book shows how to design and develop semantic web applications, data models, indexing and query processing solutions.

Enterprise Business Intelligence and Data Warehousing

  • 1st Edition
  • November 24, 2014
  • Alan Simon
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 1 5 4 0 - 7
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 1 7 4 6 - 3
Corporations and governmental agencies of all sizes are embracing a new generation of enterprise-scale business intelligence (BI) and data warehousing (DW), and very often appoint a single senior-level individual to serve as the Enterprise BI/DW Program Manager. This book is the essential guide to the incremental and iterative build-out of a successful enterprise-scale BI/DW program comprised of multiple underlying projects, and what the Enterprise Program Manager must successfully accomplish to orchestrate the many moving parts in the quest for true enterprise-scale business intelligence and data warehousing. Author Alan Simon has served as an enterprise business intelligence and data warehousing program management advisor to many of his clients, and spent an entire year with a single client as the adjunct consulting director for a $10 million enterprise data warehousing (EDW) initiative. He brings a wealth of knowledge about best practices, risk management, organizational culture alignment, and other Critical Success Factors (CSFs) to the discipline of enterprise-scale business intelligence and data warehousing.

Social Data Analytics

  • 1st Edition
  • November 10, 2014
  • Krish Krishnan + 1 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 3 9 7 1 8 6 - 9
  • eBook
    9 7 8 - 0 - 1 2 - 3 9 7 7 8 0 - 9
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

  • 1st Edition
  • November 4, 2014
  • Rick Sherman
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 4 1 1 4 6 1 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 4 1 1 5 2 8 - 6
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

  • 1st Edition
  • September 23, 2014
  • Enda Ridge
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 0 2 1 8 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 0 5 0 3 - 3
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

  • 1st Edition
  • August 28, 2014
  • Alan Simon
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 1 5 3 9 - 1
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 1 7 4 5 - 6
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

  • 1st Edition
  • August 19, 2014
  • Tom Johnston
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 4 0 8 0 6 7 - 6
  • eBook
    9 7 8 - 0 - 1 2 - 4 0 8 0 5 5 - 3
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

  • 2nd Edition
  • July 16, 2014
  • C.J. Date + 2 more
  • English
  • Paperback
    9 7 8 - 0 - 1 2 - 8 0 0 6 3 1 - 3
  • eBook
    9 7 8 - 0 - 1 2 - 8 0 0 6 7 5 - 7
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.

Concurrency Control in Distributed Database Systems

  • 1st Edition
  • Volume 3
  • June 28, 2014
  • W. Cellary + 2 more
  • English
  • eBook
    9 7 8 - 1 - 4 8 3 2 - 9 4 6 4 - 3
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.