Data Modeling Essentials

Author: Graeme Simsion
Editor: Elsevier
ISBN: 9780080488677
File Size: 79,47 MB
Format: PDF, ePub
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Data Modeling Essentials, Third Edition, covers the basics of data modeling while focusing on developing a facility in techniques, rather than a simple familiarization with "the rules". In order to enable students to apply the basics of data modeling to real models, the book addresses the realities of developing systems in real-world situations by assessing the merits of a variety of possible solutions as well as using language and diagramming methods that represent industry practice. This revised edition has been given significantly expanded coverage and reorganized for greater reader comprehension even as it retains its distinctive hallmarks of readability and usefulness. Beginning with the basics, the book provides a thorough grounding in theory before guiding the reader through the various stages of applied data modeling and database design. Later chapters address advanced subjects, including business rules, data warehousing, enterprise-wide modeling and data management. It includes an entirely new section discussing the development of logical and physical modeling, along with new material describing a powerful technique for model verification. It also provides an excellent resource for additional lectures and exercises. This text is the ideal reference for data modelers, data architects, database designers, DBAs, and systems analysts, as well as undergraduate and graduate-level students looking for a real-world perspective. Thorough coverage of the fundamentals and relevant theory. Recognition and support for the creative side of the process. Expanded coverage of applied data modeling includes new chapters on logical and physical database design. New material describing a powerful technique for model verification. Unique coverage of the practical and human aspects of modeling, such as working with business specialists, managing change, and resolving conflict.

Data Modeling For Mongodb

Author: Steve Hoberman
Editor: Technics Publications
ISBN: 1634620410
File Size: 42,26 MB
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Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application’s release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future. Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions. Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together. This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. There are three sections: In Section I, Getting Started, we will reveal the power of data modeling and the tight connections to data models that exist when designing any type of database (Chapter 1), compare NoSQL with traditional relational databases and where MongoDB fits (Chapter 2), explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts (Chapter 3), and explain the basics of adding, querying, updating, and deleting data in MongoDB (Chapter 4). In Section II, Levels of Granularity, we cover Conceptual Data Modeling (Chapter 5), Logical Data Modeling (Chapter 6), and Physical Data Modeling (Chapter 7). Notice the “ing” at the end of each of these chapters. We focus on the process of building each of these models, which is where we gain essential business knowledge. In Section III, Case Study, we will explain both top down and bottom up development approaches and go through a top down case study where we start with business requirements and end with the MongoDB database. This case study will tie together all of the techniques in the previous seven chapters. Nike Senior Data Architect Ryan Smith wrote the foreword. Key points are included at the end of each chapter as a way to reinforce concepts. In addition, this book is loaded with hands-on exercises, along with their answers provided in Appendix A. Appendix B contains all of the book’s references and Appendix C contains a glossary of the terms used throughout the text.

Advanced Standard Sql Dynamic Structured Data Modeling And Hierarchical Processing

Author: Michael M. David
Editor: Artech House
ISBN: 1608075338
File Size: 37,93 MB
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Based on the Artech House classic ANSI SQL Data Modeling and Structure Processing, this expanded and updated book offers you an essential tool for utilizing the ANSI SQL outer join operation to perform simple or complex hierarchical data modeling and structure processing. The book provides you with a comprehensive review of the outer join operation, its powerful syntax and semantics, and new features and capabilities. This revised resource introduces several important new concepts such as relationship and hierarchical integration at the hierarchical processing level, multipath hierarchical automatic XML query processing, dynamic structured data processing using automatic metadata maintenance, and advanced data transformations. Featuring more than 230 illustrations, the book shows you how to tap the full power of data structure extraction technology that gathers data structure meta information naturally embedded in ANSI SQL specifications. You discover existing, but previously unknown, SQL capabilities for improving performance. The book explains how to perform multitable outer joins and combine relational structures with hierarchical structures. Moreover you learn how to establish a default database standard for hierarchical data modeling and structure processing.

Patterns Of Data Modeling

Author: Michael Blaha
Editor: CRC Press
ISBN: 1498785646
File Size: 56,69 MB
Format: PDF, ePub
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Best-selling author and database expert with more than 25 years of experience modeling application and enterprise data, Dr. Michael Blaha provides tried and tested data model patterns, to help readers avoid common modeling mistakes and unnecessary frustration on their way to building effective data models. Unlike the typical methodology book, Patterns of Data Modeling provides advanced techniques for those who have mastered the basics. Recognizing that database representation sets the path for software, determines its flexibility, affects its quality, and influences whether it succeeds or fails, the text focuses on databases rather than programming. It is one of the first books to apply the popular patterns perspective to database systems and data models. It offers practical advice on the core aspects of applications and provides authoritative coverage of mathematical templates, antipatterns, archetypes, identity, canonical models, and relational database design.

Database Modeling And Design

Author: Toby J. Teorey
Editor: Elsevier
ISBN: 9780080470771
File Size: 29,25 MB
Format: PDF, Kindle
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Database Modeling and Design, Fourth Edition, the extensively revised edition of the classic logical database design reference, explains how you can model and design your database application in consideration of new technology or new business needs. It is an ideal text for a stand-alone data management course focused on logical database design, or a supplement to an introductory text for introductory database management. This book features clear explanations, lots of terrific examples and an illustrative case, and practical advice, with design rules that are applicable to any SQL-based system. The common examples are based on real-life experiences and have been thoroughly class-tested. The text takes a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling - complemented with examples for both approaches. It also discusses the use of data modeling concepts in logical database design; the transformation of the conceptual model to the relational model and to SQL syntax; the fundamentals of database normalization through the fifth normal form; and the major issues in business intelligence such as data warehousing, OLAP for decision support systems, and data mining. There are examples for how to use the most popular CASE tools to handle complex data modeling problems, along with exercises that test understanding of all material, plus solutions for many exercises. Lecture notes and a solutions manual are also available. This edition will appeal to professional data modelers and database design professionals, including database application designers, and database administrators (DBAs); new/novice data management professionals, such as those working on object oriented database design; and students in second courses in database focusing on design. + a detailed look at the Unified Modeling Language (UML-2) as well as the entity-relationship (ER) approach for data requirements specification and conceptual modeling--with examples throughout the book in both approaches! + the details and examples of how to use data modeling concepts in logical database design, and the transformation of the conceptual model to the relational model and to SQL syntax; + the fundamentals of database normalization through the fifth normal form; + practical coverage of the major issues in business intelligence--data warehousing, OLAP for decision support systems, and data mining; + examples for how to use the most popular CASE tools to handle complex data modeling problems. + Exercises that test understanding of all material, plus solutions for many exercises.

Data Modeling Made Simple

Author: Steve Hoberman
Editor: Technics Publications Llc
ISBN: 9780977140060
File Size: 15,72 MB
Format: PDF, Kindle
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Read today's business headlines and you will see that many issues stem from people not having the right data at the right time. Data issues don't always make the front page, yet they exist within every organisation. We need to improve how we manage data -- and the most valuable tool for explaining, vaildating and managing data is a data model. This book provides the business or IT professional with a practical working knowledge of data modelling concepts and best practices. This book is written in a conversational style that encourages you to read it from start to finish and master these ten objectives: Know when a data model is needed and which type of data model is most effective for each situation; Read a data model of any size and complexity with the same confidence as reading a book; Build a fully normalised relational data model, as well as an easily navigatable dimensional model; Apply techniques to turn a logical data model into an efficient physical design; Leverage several templates to make requirements gathering more efficient and accurate; Explain all ten categories of the Data Model Scorecard®; Learn strategies to improve your working relationships with others; Appreciate the impact unstructured data has, and will have, on our data modelling deliverables; Learn basic UML concepts; Put data modelling in context with XML, metadata, and agile development.

A Study On Conceptual Data Modeling

Author: Eva Lindencrona-Ohlin
Editor:
ISBN:
File Size: 25,15 MB
Format: PDF, Mobi
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Spatial Data Modeling For Feature Based Geographic Information Systems

Author: Agatha Yin-Shan Tang
Editor:
ISBN:
File Size: 57,41 MB
Format: PDF, ePub, Mobi
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Hyperspectral Data Modeling For Water Quality Studies In Michigan S Inland Lakes

Author: Narumon Wiangwang
Editor:
ISBN:
File Size: 11,20 MB
Format: PDF, ePub
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Data Modeling For The Business

Author: Steve Hoberman
Editor: Technics Publications Llc
ISBN: 9780977140077
File Size: 61,53 MB
Format: PDF, Kindle
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Did you ever try getting Business and IT to agree on the project scope for a new application? Or try getting the Sales & Marketing department to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse -- without them dozing off? You can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives. Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach. Know how to evaluate toolsets for building and storing your models. Practice exercises and walk through a case study to reinforce your modelling skills.

Mastering Data Modeling

Author: John Carlis
Editor: Addison-Wesley Professional
ISBN: 0134176537
File Size: 51,95 MB
Format: PDF
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Data modeling is one of the most critical phases in the database application development process, but also the phase most likely to fail. A master data modeler must come into any organization, understand its data requirements, and skillfully model the data for applications that most effectively serve organizational needs. Mastering Data Modeling is a complete guide to becoming a successful data modeler. Featuring a requirements-driven approach, this book clearly explains fundamental concepts, introduces a user-oriented data modeling notation, and describes a rigorous, step-by-step process for collecting, modeling, and documenting the kinds of data that users need. Assuming no prior knowledge, Mastering Data Modeling sets forth several fundamental problems of data modeling, such as reconciling the software developer's demand for rigor with the users' equally valid need to speak their own (sometimes vague) natural language. In addition, it describes the good habits that help you respond to these fundamental problems. With these good habits in mind, the book describes the Logical Data Structure (LDS) notation and the process of controlled evolution by which you can create low-cost, user-approved data models that resist premature obsolescence. Also included is an encyclopedic analysis of all data shapes that you will encounter. Most notably, the book describes The Flow, a loosely scripted process by which you and the users gradually but continuously improve an LDS until it faithfully represents the information needs. Essential implementation and technology issues are also covered. You will learn about such vital topics as: The fundamental problems of data modeling The good habits that help a data modeler be effective and economical LDS notation, which encourages these good habits How to read an LDS aloud--in declarative English sentences How to write a well-formed (syntactically correct) LDS How to get users to name the parts of an LDS with words from their own business vocabulary How to visualize data for an LDS A catalog of LDS shapes that recur throughout all data models The Flow--the template for your conversations with users How to document an LDS for users, data modelers, and technologists How to map an LDS to a relational schema How LDS differs from other notations and why "Story interludes" appear throughout the book, illustrating real-world successes of the LDS notation and controlled evolution process. Numerous exercises help you master critical skills. In addition, two detailed, annotated sample conversations with users show you the process of controlled evolution in action.

Data Modeling For Quality

Author: Graham Witt
Editor: Technics Publications
ISBN: 1634629159
File Size: 45,74 MB
Format: PDF, ePub, Mobi
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This book is for all data modelers, data architects, and database designers―be they novices who want to learn what’s involved in data modeling, or experienced modelers who want to brush up their skills. A novice will not only gain an overview of data modeling, they will also learn how to follow the data modeling process, including the activities required for each step. The experienced practitioner will discover (or rediscover) techniques to ensure that data models accurately reflect business requirements. This book describes rigorous yet easily implemented approaches to: · modeling of business information requirements for review by business stakeholders before development of the logical data model · normalizing data, based on simple questions rather than the formal definitions which many modelers find intimidating · naming and defining concepts and attributes · modeling of time-variant data · documenting business rules governing both the real world and data · data modeling in an Agile project · managing data model change in any type of project · transforming a business information model to a logical data model against which developers can code · implementing the logical data model in a traditional relational DBMS, an SQL:2003-compliant DBMS, an object-relational DBMS, or in XML. Part 1 describes business information models in-depth, including: · the importance of modeling business information requirements before embarking on a logical data model · business concepts (entity classes) · attributes of business concepts · attribute classes as an alternative to DBMS data types · relationships between business concepts · time-variant data · generalization and specialization of business concepts · naming and defining the components of the business information model · business rules governing data, including a distinction between real-world rules and data rules. Part 2 journeys from requirements to a working data resource, covering: · sourcing data requirements · developing the business information model · communicating it to business stakeholders for review, both as diagrams and verbally · managing data model change · transforming the business information model into a logical data model of stored data for implementation in a relational or object-relational DBMS · attribute value representation and data constraints (important but often overlooked) · modeling data vault, dimensional and XML data.

Data Modeling Fundamentals

Author: Paulraj Ponniah
Editor: Wiley-Interscience
ISBN:
File Size: 22,91 MB
Format: PDF, Kindle
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Mysql Workbench Data Modeling Development

Author: Michael McLaughlin
Editor: McGraw Hill Professional
ISBN: 0071791892
File Size: 57,65 MB
Format: PDF
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The only Oracle Press guide to MySQL Workbench explains how to design and model MySQL databases. MySQL Workbench Data Modeling and Development helps developers learn how to effectively use this powerful product for database modeling, reverse engineering, and interaction with the database without writing SQL statements. MySQL Workbench is a graphical user interface that can be used to create and maintain MySQL databases without coding. The book covers the interface and explains how to accomplish each step by illustrating best practices visually. Clear examples, instructions, and explanations reveal, in a single volume, the art of database modeling. This Oracle Press guide shows you how to get the tool to do what you want. Annotated screen shots demonstrate all interactions with the tool, and text explains the how, what, and why of each step. Complete coverage Installation and Configuration; Creating and Managing Connections; Data Modeling Concepts; Creating an ERD; Defining the Physical Schemata; Creating and Managing Tables; Creating and Managing Relationships; Creating and Managing Views; Creating and Managing Routines; Creating and Managing Routine Groups; Creating and Managing User & Groups; Creating and Managing SQL Scripts; Generating SQL Scripts; Forward Engineering a Data Model; Synchronize a Model with a Database; Reverse Engineering a Database; Managing Differences in the Data Catalog; Creating and Managing Model Notes; Editing Table Data; Editing Generated Scripts; Creating New Instances; Managing Import and Export; Managing Security; Managing Server Instances

Geosciences And Water Resources Environmental Data Modeling

Author: Claude Bardinet
Editor: Springer Science & Business Media
ISBN: 364260627X
File Size: 54,45 MB
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This volume contains selected up-to-date professional papers prepared by specialists from various disciplines related to geosciences and water resources. Thirty papers discuss different aspects of environmental data modeling. It provides a forum bringing together contributions, both theoretical and applied, with special attention to Water in Ecosystems, Global Atmospheric Evolution, Space and Earth Remote Sensing, Regional Environmental Changes, Accessing Geoenvironmental Data and Ecotoxicological Issues. "Geosciences and Water Resources: Environmental Data Modeling" is now the fourth volume in the Series "Data and Knowledge in a Changing World". Launched by CODATA after the 14th International Conference of the Committee on Data for Sciences and Technology, in Chambery, the purpose of this new Series is to collect from widely varying fields a wealth of information pertaining to the intelligent exploitation of data in science and technology and to make that information available to a multidisciplinary community. The present series encompasses a broad range of contributions, including computer-related handling and visualization of data, to the major scientific, tech nical, medical and social fields. The titles of the previous published volumes are: The Information Revolution: Impact on Science and Technology. Modeling Complex Data for Creating Information. Industrial Information and Design Issues.

Data Modeling Made Simple With Ca Erwin Data Modeler R8

Author: Donna Burbank
Editor: Technics Publications Llc
ISBN: 9781935504092
File Size: 34,84 MB
Format: PDF, Mobi
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Learn how to do data modeling using CA ERwin Data Modeler r8!

Proceedings Of The National Conference On Computing For Nation Development

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Editor:
ISBN:
File Size: 60,37 MB
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Modeling And Simulation Proceedings Of The Annual Pittsburgh Conference

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ISBN:
File Size: 20,16 MB
Format: PDF, Kindle
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Semantic Data Modeling

Author: J. H. ter Bekke
Editor:
ISBN:
File Size: 13,94 MB
Format: PDF, ePub, Docs
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This is an introduction to semantic data modelling which discusses the basis and consequences of semantic data modelling principles. Semantic data modelling is explained by referring to a large number of practical cases, demonstrating how practical use can be made of the advantages of semantic principles in both relational and network environments.

Beginning Relational Data Modeling

Author: Sharon Allen
Editor:
ISBN:
File Size: 76,29 MB
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A guide to data modeling provides information on the process of capturing business information, formulating data models, and mapping models to a physical relational database design.