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| conceptual data model diagram: Handbook of Relational Database Design Candace C. Fleming, Barbara Von Halle, 1989 This book provides a practical and proven approach to designing relational databases. It contains two complementary design methodologies: logical data modeling and relational database design. The design methodologies are independent of product-specific implementations and have been applied to numerous relational product environments. 0201114348B04062001 |
| conceptual data model diagram: Domain-driven Design Eric Evans, 2004 Domain-Driven Design incorporates numerous examples in Java-case studies taken from actual projects that illustrate the application of domain-driven design to real-world software development. |
| conceptual data model diagram: Business analyst: a profession and a mindset Yulia Kosarenko, 2019-05-12 What does it mean to be a business analyst? What would you do every day? How will you bring value to your clients? And most importantly, what makes a business analyst exceptional? This book will answer your questions about this challenging career choice through the prism of the business analyst mindset — a concept developed by the author, and its twelve principles demonstrated through many case study examples. Business analyst: a profession and a mindset is a structurally rich read with over 90 figures, tables and models. It offers you more than just techniques and methodologies. It encourages you to understand people and their behaviour as the key to solving business problems. |
| conceptual data model diagram: Conceptual Data Modeling and Database Design: A Fully Algorithmic Approach, Volume 1 Christian Mancas, 2016-01-05 This new book aims to provide both beginners and experts with a completely algorithmic approach to data analysis and conceptual modeling, database design, implementation, and tuning, starting from vague and incomplete customer requests and ending with IBM DB/2, Oracle, MySQL, MS SQL Server, or Access based software applications. A rich panoply of s |
| conceptual data model diagram: Data Modeling Fundamentals Paulraj Ponniah, 2007-06-30 The purpose of this book is to provide a practical approach for IT professionals to acquire the necessary knowledge and expertise in data modeling to function effectively. It begins with an overview of basic data modeling concepts, introduces the methods and techniques, provides a comprehensive case study to present the details of the data model components, covers the implementation of the data model with emphasis on quality components, and concludes with a presentation of a realistic approach to data modeling. It clearly describes how a generic data model is created to represent truly the enterprise information requirements. |
| conceptual data model diagram: Data Modeling Essentials Graeme Simsion, Graham Witt, 2004-12-03 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 |
| conceptual data model diagram: Developing High Quality Data Models Matthew West, 2011-02-07 Developing High Quality Data Models provides an introduction to the key principles of data modeling. It explains the purpose of data models in both developing an Enterprise Architecture and in supporting Information Quality; common problems in data model development; and how to develop high quality data models, in particular conceptual, integration, and enterprise data models. The book is organized into four parts. Part 1 provides an overview of data models and data modeling including the basics of data model notation; types and uses of data models; and the place of data models in enterprise architecture. Part 2 introduces some general principles for data models, including principles for developing ontologically based data models; and applications of the principles for attributes, relationship types, and entity types. Part 3 presents an ontological framework for developing consistent data models. Part 4 provides the full data model that has been in development throughout the book. The model was created using Jotne EPM Technologys EDMVisualExpress data modeling tool. This book was designed for all types of modelers: from those who understand data modeling basics but are just starting to learn about data modeling in practice, through to experienced data modelers seeking to expand their knowledge and skills and solve some of the more challenging problems of data modeling. - Uses a number of common data model patterns to explain how to develop data models over a wide scope in a way that is consistent and of high quality - Offers generic data model templates that are reusable in many applications and are fundamental for developing more specific templates - Develops ideas for creating consistent approaches to high quality data models |
| conceptual data model diagram: Information Modeling and Relational Databases Terry Halpin, Tony Morgan, 2024-07-22 Information Modeling and Relational Databases, Third Edition, provides an introduction to ORM (Object-Role Modeling) and much more. In fact, it is the only book to go beyond introductory coverage and provide all of the in-depth instruction you need to transform knowledge from domain experts into a sound database design. This book is intended for anyone with a stake in the accuracy and efficacy of databases: systems analysts, information modelers, database designers and administrators, and programmers. Dr. Terry Halpin and Dr. Tony Morgan, pioneers in the development of ORM, blend conceptual information with practical instruction that will let you begin using ORM effectively as soon as possible. The all-new Third Edition includes coverage of advances and improvements in ORM and UML, nominalization, relational mapping, SQL, XML, data interchange, NoSQL databases, ontological modeling, and post-relational databases. Supported by examples, exercises, and useful background information, the authors' step-by-step approach teaches you to develop a natural-language-based ORM model, and then, where needed, abstract ER and UML models from it. This book will quickly make you proficient in the modeling technique that is proving vital to the development of accurate and efficient databases that best meet real business objectives. This book is an excellent introduction to both information modeling in ORM and relational databases. The book is very clearly written in a step-by-step manner and contains an abundance of well-chosen examples illuminating practice and theory in information modeling. I strongly recommend this book to anyone interested in conceptual modeling and databases. — Dr. Herman Balsters, Director of the Faculty of Industrial Engineering, University of Groningen, The Netherlands - Presents the most in-depth coverage of object-role modeling, including a thorough update of the book for the latest versions of ORM, ER, UML, OWL, and BPMN modeling. - Includes clear coverage of relational database concepts as well as the latest developments in SQL, XML, information modeling, data exchange, and schema transformation. - Case studies and a large number of class-tested exercises are provided for many topics. - Includes all-new chapters on data file formats and NoSQL databases. |
| conceptual data model diagram: Conceptual Database Design Carlo Batini, Stefano Ceri, Shamkant B. Navathe, 1992 This database design book provides the reader with a unique methodology for the conceptual and logical design of databases. A step-by-step method is given for developing a conceptual structure for large databases with multiple users. Additionally, the authors provide an up-to-date survey and analysis of existing database design tools. |
| conceptual data model diagram: Principles of Database Management Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens, 2018-07-12 Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science. |
| conceptual data model diagram: Learning MySQL Seyed Tahaghoghi, Hugh E. Williams, 2007-11-28 This new book in the popular Learning series offers an easy-to-use resource for newcomers to the MySQL relational database. This tutorial explains in plain English how to set up MySQL and related software from the beginning, and how to do common tasks. |
| conceptual data model diagram: Mastering Data Modeling John Carlis, 2000-11-10 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. |
| conceptual data model diagram: Business Intelligence Guidebook Rick Sherman, 2014-11-04 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. - Provides practical guidelines for building successful BI, DW and data integration solutions. - Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. - Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses - Describes best practices and pragmatic approaches so readers can put them into action. - Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources. |
| conceptual data model diagram: Data Modeling and Database Design Narayan S. Umanath, Richard W. Scamell, 2014-06-18 DATA MODELING AND DATABASE DESIGN presents a conceptually complete coverage of indispensable topics that each MIS student should learn if that student takes only one database course. Database design and data modeling encompass the minimal set of topics addressing the core competency of knowledge students should acquire in the database area. The text, rich examples, and figures work together to cover material with a depth and precision that is not available in more introductory database books. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| conceptual data model diagram: Handbook of Conceptual Modeling David W. Embley, Bernhard Thalheim, 2012-04-23 Conceptual modeling is about describing the semantics of software applications at a high level of abstraction in terms of structure, behavior, and user interaction. Embley and Thalheim start with a manifesto stating that the dream of developing information systems strictly by conceptual modeling – as expressed in the phrase “the model is the code” – is becoming reality. The subsequent contributions written by leading researchers in the field support the manifesto's assertions, showing not only how to abstractly model complex information systems but also how to formalize abstract specifications in ways that let developers complete programming tasks within the conceptual model itself. They are grouped into sections on programming with conceptual models, structure modeling, process modeling, user interface modeling, and special challenge areas such as conceptual geometric modeling, information integration, and biological conceptual modeling. The Handbook of Conceptual Modeling collects in a single volume many of the best conceptual-modeling ideas, techniques, and practices as well as the challenges that drive research in the field. Thus it is much more than a traditional handbook for advanced professionals, as it also provides both a firm foundation for the field of conceptual modeling, and points researchers and graduate students towards interesting challenges and paths for how to contribute to this fundamental field of computer science. |
| conceptual data model diagram: The Entity-Relationship Model Peter Pin-Shan Chen, 2016-12-06 Excerpt from The Entity-Relationship Model: A Basis for the Enterprise View of Data The subject of the logical view of data has attracted considerable attention in the past ten years. However, most researchers have focused on the user View of data. The need for studying the enterprise view of data was not recognized until recently. Different users of a database may have different views of the database, but the enterprise should have a unique and consistent view of the database. This is particularly important in designing a logically meaningful and consistent database. The concept of the enterprise View of data is very useful in the database design process and in. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works. |
| conceptual data model diagram: Conceptual Modeling of Information Systems Antoni Olivé, 2007-08-15 This brilliant textbook explains in detail the principles of conceptual modeling independently from particular methods and languages and shows how to apply them in real-world projects. The author covers all aspects of the engineering process from structural modeling over behavioral modeling to meta-modeling, and completes the presentation with an extensive case study based on the osCommerce system. Written for computer science students in classes on information systems modeling as well as for professionals feeling the need to formalize their experiences or to update their knowledge, Olivé delivers here a comprehensive treatment of all aspects of the modeling process. His book is complemented by lots of exercises and additional online teaching material. |
| conceptual data model diagram: Data Model Patterns: A Metadata Map David C. Hay, 2010-07-20 Data Model Patterns: A Metadata Map not only presents a conceptual model of a metadata repository but also demonstrates a true enterprise data model of the information technology industry itself. It provides a step-by-step description of the model and is organized so that different readers can benefit from different parts. It offers a view of the world being addressed by all the techniques, methods, and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) and presents several concepts that need to be addressed by such tools. This book is pertinent, with companies and government agencies realizing that the data they use represent a significant corporate resource recognize the need to integrate data that has traditionally only been available from disparate sources. An important component of this integration is management of the metadata that describe, catalogue, and provide access to the various forms of underlying business data. The metadata repository is essential to keep track of the various physical components of these systems and their semantics. The book is ideal for data management professionals, data modeling and design professionals, and data warehouse and database repository designers. - A comprehensive work based on the Zachman Framework for information architecture—encompassing the Business Owner's, Architect's, and Designer's views, for all columns (data, activities, locations, people, timing, and motivation) - Provides a step-by-step description of model and is organized so that different readers can benefit from different parts - Provides a view of the world being addressed by all the techniques, methods and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) - Presents many concepts that are not currently being addressed by such tools — and should be |
| conceptual data model diagram: Cassandra: The Definitive Guide Jeff Carpenter, Eben Hewitt, 2016-06-29 Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition—updated for Cassandra 3.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data Maintain a high level of performance in your cluster Deploy Cassandra on site, in the Cloud, or with Docker Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene |
| conceptual data model diagram: The Object Primer Scott W. Ambler, 2004-03-22 The acclaimed beginner's book on object technology now presents UML 2.0, Agile Modeling, and object development techniques. |
| conceptual data model diagram: LogiQL Terry Halpin, Spencer Rugaber, 2014-11-13 LogiQL is a new state-of-the-art programming language based on Datalog. It can be used to build applications that combine transactional, analytical, graph, probabilistic, and mathematical programming. LogiQL makes it possible to build hybrid applications that previously required multiple programming languages and databases. In this first book to co |
| conceptual data model diagram: Syntactic Structures Noam Chomsky, 2020-05-18 No detailed description available for Syntactic Structures. |
| conceptual data model diagram: Agile Database Techniques Scott Ambler, 2012-09-17 Describes Agile Modeling Driven Design (AMDD) and Test-Driven Design (TDD) approaches, database refactoring, database encapsulation strategies, and tools that support evolutionary techniques Agile software developers often use object and relational database (RDB) technology together and as a result must overcome the impedance mismatch The author covers techniques for mapping objects to RDBs and for implementing concurrency control, referential integrity, shared business logic, security access control, reports, and XML An agile foundation describes fundamental skills that all agile software developers require, particularly Agile DBAs Includes object modeling, UML data modeling, data normalization, class normalization, and how to deal with legacy databases Scott W. Ambler is author of Agile Modeling (0471202827), a contributing editor with Software Development (www.sdmagazine.com), and a featured speaker at software conferences worldwide |
| conceptual data model diagram: Semantic Modeling for Data Panos Alexopoulos, 2020-08-19 What value does semantic data modeling offer? As an information architect or data science professional, let’s say you have an abundance of the right data and the technology to extract business gold—but you still fail. The reason? Bad data semantics. In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You’ll learn how to master this craft to increase the usability and value of your data and applications. You’ll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data. Understand the fundamental concepts, phenomena, and processes related to semantic data modeling Examine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and tools Avoid mistakes and bad practices that can undermine your efforts to create good data models Learn about model development dilemmas, including representation, expressiveness and content, development, and governance Organize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges |
| conceptual data model diagram: Beginning Database Design Clare Churcher, 2012-08-08 Beginning Database Design, Second Edition provides short, easy-to-read explanations of how to get database design right the first time. This book offers numerous examples to help you avoid the many pitfalls that entrap new and not-so-new database designers. Through the help of use cases and class diagrams modeled in the UML, you’ll learn to discover and represent the details and scope of any design problem you choose to attack. Database design is not an exact science. Many are surprised to find that problems with their databases are caused by poor design rather than by difficulties in using the database management software. Beginning Database Design, Second Edition helps you ask and answer important questions about your data so you can understand the problem you are trying to solve and create a pragmatic design capturing the essentials while leaving the door open for refinements and extension at a later stage. Solid database design principles and examples help demonstrate the consequences of simplifications and pragmatic decisions. The rationale is to try to keep a design simple, but allow room for development as situations change or resources permit. Provides solid design principles by which to avoid pitfalls and support changing needs Includes numerous examples of good and bad design decisions and their consequences Shows a modern method for documenting design using the Unified Modeling Language |
| conceptual data model diagram: Database Life Cycle Open University. Relational Databases: Theory and Practice Course Team, 2007-04 This block is concerned with the database lifecycle, which describes the stages a database goes through, from the time the need for a database is established until it is withdrawn from use. This block applies the practice developed in Block 3 to systematically develop, implement and maintain a database design that supports the information requirements of an enterprise. It presents a simple framework for database development and maintenance.This is a very practical block and will require you to write and execute SQL statements for which you will need access to a computer installed with the course software (order code M359/CDR01) and database cards Scenarios and Hospital conceptual data model (order code M359/DBCARDS) |
| conceptual data model diagram: UML and Data Modeling David Hay, 2011-11 Here you will learn how to develop an attractive easily readable conceptual business-oriented entity/relationship model using a variation on the UML Class Model notation. This book has two audiences: Data modelers (both analysts and database designers) who are convinced that UML has nothing to do with them; and UML experts who don't realize that architectural data modeling really is different from object modeling (and that the differences are important). David Hay's objective is to finally bring these two groups together in peace. Here all modelers will receive guidance on how to produce a high quality (that is readable) entity/relationship model to describe the data architecture of an organization. The notation involved happens to be the one for class models in the Unified Modeling Language even though UML was originally developed to support object-oriented design. Designers have a different view of the world from those who develop business-oriented conceptual data models which means that to use UML for architectural modeling requires some adjustments. These adjustments are described in this book. David Hay is the author of Enterprise Model Patterns: Describing the World a comprehensive model of a generic enterprise. The diagrams were at various levels of abstraction and they were all rendered in the slightly modified version of UML Class Diagrams presented here. This book is a handbook to describe how to build models such as these. By way of background an appendix provides a history of the two groups revealing the sources of their different attitudes towards the system development process. |
| conceptual data model diagram: Data Modeling, A Beginner's Guide Andy Oppel, 2009-11-23 Essential Skills--Made Easy! Learn how to create data models that allow complex data to be analyzed, manipulated, extracted, and reported upon accurately. Data Modeling: A Beginner's Guide teaches you techniques for gathering business requirements and using them to produce conceptual, logical, and physical database designs. You'll get details on Unified Modeling Language (UML), normalization, incorporating business rules, handling temporal data, and analytical database design. The methods presented in this fast-paced tutorial are applicable to any database management system, regardless of vendor. Designed for Easy Learning Key Skills & Concepts--Chapter-opening lists of specific skills covered in the chapter Ask the expert--Q&A sections filled with bonus information and helpful tips Try This--Hands-on exercises that show you how to apply your skills Notes--Extra information related to the topic being covered Self Tests--Chapter-ending quizzes to test your knowledge Andy Oppel has taught database technology for the University of California Extension for more than 25 years. He is the author of Databases Demystified, SQL Demystified, and Databases: A Beginner's Guide, and the co-author of SQL: A Beginner's Guide, Third Edition, and SQL: The Complete Reference, Third Edition. |
| conceptual data model diagram: Design of Industrial Information Systems Thomas Boucher, Ali Yalcin, 2010-07-26 Design of Industrial Information Systems presents a body of knowledge applicable to many aspects of industrial and manufacturing systems. New software systems, such as Enterprise Resource Planning, and new hardware technologies, such as RFID, have made it possible to integrate what were separate IT databases and operations into one system to realize the greatest possible operational efficiencies. This text provides a background in, and an introduction to, the relevant information technologies and shows how they are used to model and implement integrated IT systems.With the growth of courses in information technology offered in industrial engineering and engineering management programs, the authors have written this book to show how such computer-based knowledge systems are designed and used in modern manufacturing and industrial companies. - Introduces Data Modeling and Functional Architecture Design, with a focus on integration for overall system design - Encompasses hands-on approach, employing many in-chapter exercises and end-of-chapter problem sets with case studies in manufacturing and service industries - Shows the reader how Information Systems can be integrated into a wider E-business/Web-Enabled Database business model - Offers applications in Enterprise Resource Planning (ERP) and Manufacturing Execution Systems (MES) |
| conceptual data model diagram: Refactoring Databases Scott W. Ambler, Pramod J. Sadalage, 2006-03-03 Refactoring has proven its value in a wide range of development projects–helping software professionals improve system designs, maintainability, extensibility, and performance. Now, for the first time, leading agile methodologist Scott Ambler and renowned consultant Pramodkumar Sadalage introduce powerful refactoring techniques specifically designed for database systems. Ambler and Sadalage demonstrate how small changes to table structures, data, stored procedures, and triggers can significantly enhance virtually any database design–without changing semantics. You’ll learn how to evolve database schemas in step with source code–and become far more effective in projects relying on iterative, agile methodologies. This comprehensive guide and reference helps you overcome the practical obstacles to refactoring real-world databases by covering every fundamental concept underlying database refactoring. Using start-to-finish examples, the authors walk you through refactoring simple standalone database applications as well as sophisticated multi-application scenarios. You’ll master every task involved in refactoring database schemas, and discover best practices for deploying refactorings in even the most complex production environments. The second half of this book systematically covers five major categories of database refactorings. You’ll learn how to use refactoring to enhance database structure, data quality, and referential integrity; and how to refactor both architectures and methods. This book provides an extensive set of examples built with Oracle and Java and easily adaptable for other languages, such as C#, C++, or VB.NET, and other databases, such as DB2, SQL Server, MySQL, and Sybase. Using this book’s techniques and examples, you can reduce waste, rework, risk, and cost–and build database systems capable of evolving smoothly, far into the future. |
| conceptual data model diagram: Database Modeling and Design Toby J. Teorey, Sam S. Lightstone, Tom Nadeau, H.V. Jagadish, 2011-02-10 Includes bonus chapters from the book, Physical database design. |
| conceptual data model diagram: Software Pioneers Manfred Broy, Ernst Denert, 2012-12-06 A lucid statement of the philosophy of modular programming can be found in a 1970 textbook on the design of system programs by Gouthier and Pont [1, l Cfl0. 23], which we quote below: A well-defined segmentation of the project effort ensures system modularity. Each task fonos a separate, distinct program module. At implementation time each module and its inputs and outputs are well-defined, there is no confusion in the intended interface with other system modules. At checkout time the in tegrity of the module is tested independently; there are few sche duling problems in synchronizing the completion of several tasks before checkout can begin. Finally, the system is maintained in modular fashion; system errors and deficiencies can be traced to specific system modules, thus limiting the scope of detailed error searching. Usually nothing is said about the criteria to be used in dividing the system into modules. This paper will discuss that issue and, by means of examples, suggest some criteria which can be used in decomposing a system into modules. A Brief Status Report The major advancement in the area of modular programming has been the development of coding techniques and assemblers which (1) allow one modu1e to be written with little knowledge of the code in another module, and (2) alJow modules to be reas sembled and replaced without reassembly of the whole system. |
| conceptual data model diagram: Model Driven Engineering Languages and Systems Jon Whittle, Tony Clark, Thomas Kühne, 2011-10-12 This book constitutes the refereed proceedings of the 14th International Conference on Model Driven Engineering Languages and Systems, MODELS 2011, held in Wellington, New Zealand, in October 2011. The papers address a wide range of topics in research (foundations track) and practice (applications track). For the first time a new category of research papers, vision papers, are included presenting outside the box thinking. The foundations track received 167 full paper submissions, of which 34 were selected for presentation. Out of these, 3 papers were vision papers. The application track received 27 submissions, of which 13 papers were selected for presentation. The papers are organized in topical sections on model transformation, model complexity, aspect oriented modeling, analysis and comprehension of models, domain specific modeling, models for embedded systems, model synchronization, model based resource management, analysis of class diagrams, verification and validation, refactoring models, modeling visions, logics and modeling, development methods, and model integration and collaboration. |
| conceptual data model diagram: The Data Model Resource Book, Volume 1 Len Silverston, 2011-08-08 A quick and reliable way to build proven databases for core business functions Industry experts raved about The Data Model Resource Book when it was first published in March 1997 because it provided a simple, cost-effective way to design databases for core business functions. Len Silverston has now revised and updated the hugely successful 1st Edition, while adding a companion volume to take care of more specific requirements of different businesses. This updated volume provides a common set of data models for specific core functions shared by most businesses like human resources management, accounting, and project management. These models are standardized and are easily replicated by developers looking for ways to make corporate database development more efficient and cost effective. This guide is the perfect complement to The Data Model Resource CD-ROM, which is sold separately and provides the powerful design templates discussed in the book in a ready-to-use electronic format. A free demonstration CD-ROM is available with each copy of the print book to allow you to try before you buy the full CD-ROM. |
| conceptual data model diagram: The Science of Citizen Science Katrin Vohland, Anne Land-zandstra, Luigi Ceccaroni, Rob Lemmens, Josep Perelló, Marisa Ponti, Roeland Samson, Katherin Wagenknecht, 2021 This open access book discusses how the involvement of citizens into scientific endeavors is expected to contribute to solve the big challenges of our time, such as climate change and the loss of biodiversity, growing inequalities within and between societies, and the sustainability turn. The field of citizen science has been growing in recent decades. Many different stakeholders from scientists to citizens and from policy makers to environmental organisations have been involved in its practice. In addition, many scientists also study citizen science as a research approach and as a way for science and society to interact and collaborate. This book provides a representation of the practices as well as scientific and societal outcomes in different disciplines. It reflects the contribution of citizen science to societal development, education, or innovation and provides and overview of the field of actors as well as on tools and guidelines. It serves as an introduction for anyone who wants to get involved in and learn more about the science of citizen science. |
| conceptual data model diagram: Pragmatic Enterprise Architecture James Luisi, 2014-03-15 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. - Expand your Enterprise Architecture skills so you can do more in less time with less money with the priceless tips presented - Understand the cost of creating new Enterprise Architecture disciplines and contrast those costs to letting them go unmanaged - Includes 10 life cycle architectures so that you can properly assess the ROI of performing activities such as outsourcing, insourcing, restructuring, mergers and acquisitions, and more - Complete appendix of eight transaction path analysis frameworks provide DBA guidelines for proper physical database design |
| conceptual data model diagram: Modeling and Analysis of Enterprise and Information Systems Qing Li, 2009 |
| conceptual data model diagram: Object-Role Modeling Fundamentals Terry Halpin, 2015-04-15 Object-Role Modeling (ORM) is a fact-based approach to data modeling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (e.g. Person smokes), binary (e.g. Person was born on Date), ternary (e.g. Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML). All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modeled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORM’s graphical notation. For the data modeler, ORM’s graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualization of the underlying semantics. Suitable for both novices and experienced practitioners, this book covers the fundamentals of the ORM approach. Written in easy-to-understand language, it shows how to design an ORM model, illustrating each step with simple examples. Each chapter ends with a practical lab that discusses how to use the freeware NORMA tool to enter ORM models and use it to automatically generate verbalizations of the model and map it to a relational database. |
| conceptual data model diagram: Database Design Using Entity-Relationship Diagrams Sikha Bagui, Richard Earp, 2003-06-27 Entity-relationship (E-R) diagrams are time-tested models for database development well-known for their usefulness in mapping out clear database designs. Also commonly known is how difficult it is to master them. With this comprehensive guide, database designers and developers can quickly learn all the ins and outs of E-R diagramming to become expe |
| conceptual data model diagram: SQL Server Transaction Log Management Tony Davis, Gail Shaw, 2012 This book provides an understanding of the SQL Server transaction log, what it does and how it works, and its role in ensuring data integrity. Topics covered include: write ahead logging; how to perform transaction log backup and restore in FULL and BULK_LOGGED recovery models; managing log growth, and dealing correctly with an overgrown or full log; and optimizing log throughput and availability, and how to avoid log fragmentation. -- |
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Figure 5: Business Model - Business Model Canvas (BMC). A Business Model Canvas (BMC) -diagram can be used for modelling a business model or a business case. This diagram type is …
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CSDM Conceptual Data Model EBOOK. 3 So, what is a service? So far, we’ve played fast and loose with the term “service”. However, the CSDM identifies ... As you can see in this CDSM …
DIV-1 - Conceptual Data Model - Visual Paradigm …
Creating a Conceptual Data Model To create a Conceptual Data Model: 1. Click on Conceptual Data Model in the Action Artifact area, and then select Create New Diagram. 2. Type the …
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¤ Conceptual Data Model (CDM) to model the overall logical structure of a database, independent from any software or data storage structure ... ¤ Free Model (FEM) to create any kind of chart …
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Conceptual Data Models 4 So I think the key thing is to solve a real problem, to solve the ... domain knowledge to be included in a diagram than is attainable with mind maps. More …
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Entity-Relationship Data Model Entity-Relationship Data Model † Classical, popular conceptual data model † First introduced (mid 70’s) as a (relatively minor) improvement to the relational …
Data Modeling Using the Entity-Relationship (ER) Model
Using High-Level Conceptual Data Models (cont’d.) Conceptual schema Conceptual design Description of data requirements Includes detailed descriptions of the entity types, …
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Vào menu File/New Model… Trong hộp thoại New Model, chọn Conceptual Data Model, chọn Conceptual Diagram. 1.2 Thanh công cụ Palette RelationShip (quan hệ giữa các thực thể) …
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Conceptual Data, Information Structure Viewpoint Logical Data Information Structure Viewpoint Applications ... TOGAF: Conceptual Data Diagram (aka business data model) to depict the …
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Definition: An Entity Relationship Diagram is a logical diagram representing the database structure using the relational model. N.B.! There are different notations or ERDs, for the …
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11 2. Conceptual Data Model (CDM) Conceptual Data Model (CDM) adalah model yang digunakan untuk memodelkan struktur dari keseluruhan aplikasi basis data secara detail dan …
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Data Modeling Using the Entity-Relationship (ER) Model • Entity-Relationship (ER) model – Popular high-level conceptual data model • ER diagrams – Diagrammatic notation associated …
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Jul 30, 2015 · managed in a data rich environment • Shifting to model-based enables engineering teams to more readily understand design change impacts, communicate design intent and …
The Entity-Relationship Model - University of …
Summary of Conceptual Design Conceptual design follows requirements analysis, Yields a high-level description of data to be stored ER model popular for conceptual design Constructs are …
Conceptual Database Design Using the Entity-Relationship …
Entity-Relationship Model is a different model than the Relational Model • Relation modelhas: –tables(relations) with attributes, keys, foreign keys, domain definitions for attributes • Entity …
Conceptual and Logical Database Design - University of …
1.Conceptual design The database design process starts by model-ing the application data we want to store in the database using the entity-relationship diagram 1 or the Unified Modeling …
Module 2: Conceptual Modeling Topic 1: Learning how to use …
“The key of successful conceptual modeling is translating various business rules into conceptual data model components or Entity Relationship Diagram (ERD). You won’t be able to learn it all …
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Conceptual Data Model A Conceptual data model is the most abstract form of data model. It is helpful for communicating ideas to a wide range of stakeholders because of its simplicity. …
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CDM atau Conceptual Data Model merupakan model data yang dibuat dari beberapa entity dan serta hubungan-hubungan (relationship) dari entitas-entitas tersebut. CDM dalam …
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THE CONCEPTUAL DATA MODELING PROCESS Develop a data model for the current system. Develop (or purchase) a new conceptual data model that includes all requirements of the new …
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The Entity-Relationship Diagram model is a conceptual data model developed by Chen (1976) to facilitate database design. A conceptual data model describes the structure of a database and …
Building Data Models in MS Visio 2010 Prepared by Alberto …
Nov 10, 2013 · To build a data model like this in Visio, here are the steps: 1. Do a quick sketch of your data model on paper. Just draw a box for each entity and link them with lines …
CSC343 -- Introduction to Databases Week 6: The Entity …
The Entity-Relationship Model -- 2 CSC343 -- Introduction to Databases The Entity Relationship Model The Entity-Relationship (ER) model is a conceptual data model, capable of describing …
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You can use the Entity Relationship diagram for DIV-1 Conceptual Data Model 51 DIV-2 Logical Data Model 52 DIV-3 Physical Data Model 54 Services Viewpoint 56 SvcV-1 Services Context …
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Conceptual Data Model T he conceptual multidimensional data model is the foundation for multidimensional databases. All the components and architecture of a multidimen-sional …
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A data model is a high level description of the data it can help in early analysis of storage cost, data quality There are two popular models of spatial information Field based and Object based …
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In many cases, an initial conceptual data model is drafted that does not include all data attributes. Once a satisfactory conceptual data model has been developed, the database analyst may …
Chapter 3: Data Modeling and Design – p1 – Introduction
Gathering Info. for Conceptual Data Modeling 1. Top-Down Approach • Top-down approach: o derives business rules for a data model from proper understanding of nature of business o …
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•Next, a new conceptual data model is built that includes all the requirements of the new system •In the design stage, the conceptual data model is ... –A rule to avoid confusion with Data Flow …
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Model data semantics and structure, independent of logical data model ER model / diagram #3 Logical Design (next lecture) Model data with implementation primitives of concrete data …
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Dec 31, 2009 · Lifecycle Properties.....193 Phases (PDM).....195
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the Entity Data Model (EDM). Example: The diagram below shows a conceptual model with three entity types: Book, Publisher, and Author: Figure 1.3 Conceptual model Entity Set • It is set of …
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Model data semantics and structure, independent of logical data model ER model / diagram #3 Logical Design (lecture 03, exercise 1) Model data with implementation primitives of concrete …
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The Conceptual Data Model diagram illustrates the major entities about which the business enterprise needs information. This diagram will assist in the identification of existing data …
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Big Data described by a conceptual model (UML class diagram) in a NoSQL system; this involves transforming the conceptual model into a data model compatible with NoSQL systems. To …
2. Conceptual Modeling using the Entity-Relationship …
What does Conceptual Design include? Ideas ! High-level design! Relational database schema! Relational DBMS Entity-Relationship model is used in the conceptual design of a database (+ …
Entity Relationship Modeling Standards and Guidelines
ensure data integrity. Physical Data Model (PDM) or Server Model Diagram (SMD): • A physical data model is a detailed representation of how data is structured and stored in the database …
The Enhanced Entity- Relationship (EER) Model - Georgia …
Data Abstraction, Knowledge Representation, and Ontology Concepts Goal of knowledge representation (KR) techniques Accurately model some domain of knowledge Create an …
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Data models are the conceptual models that describe the structures of databases. The structure of a database is defined by the data types, the constraints and the relationships for the …
Database Models - Sparx Systems
Conceptual Data Model A Conceptual data model is the most abstract form of data model. It is helpful for communicating ideas to a wide range of stakeholders because of its simplicity. …
CONCEPTUAL DATA-BASE DESIGN IN CONSTRUCTION …
the relational data model and the normalization process. Examples from the con ... I The outcome of the NIAM design phase is a graphical conceptual data I schema diagram that is viewed as …
Physical Data Model - SAP
Conceptual Data Model User’s Guide To work with a CDM. Object Oriented Model Getting Started To learn the basics of the OOM. Object Oriented Model User’s Guide To work with an OOM.
