Object-Role Modeling Workbook: Data Modeling Exercises using ORM and NORMA

Technics Publications
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 Written as a sequel to the author’s previous book Object-Role Modeling Fundamentals, this book briefly reviews the fundamentals of ORM, and then discusses additional topics such as model reports generation, vocabulary glossaries, relational mapping options, annotated relational schemas, schema optimization, and data modeling patterns. Written in easy-to-understand language, it illustrates each topic with simple examples, and explains how to use the freeware NORMA tool to implement the ideas discussed. The book also includes many practical exercises to promote expertise in the techniques covered, with answers provided to all the exercise questions.
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About the author

 Dr Terry Halpin is internationally recognized as the leading authority on ORM. Currently a data modeling consultant and an adjunct professor in computer science, he has many years of experience in developing and teaching data modeling technology in both industry and academia. He has authored over 200 technical publications and nine books, and has co-edited nine books on information systems modeling research. He is a an associate editor or reviewer for several academic journals, is a regular columnist for the Business Rules Journal, and is a recipient of the DAMA International Achievement Award for Education and the IFIP Outstanding Service Award.

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Additional Information

Publisher
Technics Publications
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Published on
Jan 4, 2016
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Pages
200
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ISBN
9781634621069
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Language
English
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Genres
Computers / Data Modeling & Design
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Content Protection
This content is DRM protected.
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Available on Android devices
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Master a graph data modeling technique superior to traditional data modeling for both relational and NoSQL databases (graph, document, key-value, and column), leveraging cognitive psychology to improve big data designs.

From Karen Lopez’s Foreword:

In this book, Thomas Frisendal raises important questions about the continued usefulness of traditional data modeling notations and approaches:

Are Entity Relationship Diagrams (ERDs) relevant to analytical data requirements? Are ERDs relevant in the new world of Big Data? Are ERDs still the best way to work with business users to understand their needs? Are Logical and Physical Data Models too closely coupled? Are we correct in using the same notations for communicating with business users and developers? Should we refine our existing notations and tools to meet these new needs, or should we start again from a blank page? What new notations and approaches will we need? How will we use those to build enterprise database systems?

Frisendal takes us through the history of data modeling, enterprise data models and traditional modeling methods. He points out, quite contentiously, where he feels we have gone wrong and in a few places where we got it right. He then maps out the psychology of meaning and context, while identifying important issues about where data modeling may or may not fit in business modeling. The main subject of this work is a proposal for a new exploration-driven modeling approach and new modeling notations for business concept models, business solutions models, and physical data models with examples on how to leverage those for implementing into any target database or datastore. These new notations are based on a property graph approach to modeling data.

 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.

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