- Author:
- Matthew West
A multitude of problems is likely to arise when developing data models. With dozens of attributes and millions of rows, data modelers are always in danger of inconsistency and inaccuracy. The development of the data model itself could result in difficulties presenting accurate data. The need to improve data models begins with getting it right in the first place. Using real-world examples, Developing High Quality Data Models walks the reader through identifying a number of data modeling principles and analysis techniques that enable the development of data models that both meet business requirements and have a consistent basis. The reader is presented with a variety of generic data model patterns that both exemplify the principles and techniques discussed and build upon one another to give a powerful and integrated generic data model. This model has wide applicability across many disciplines in government and industry, including but not limited to energy exploration, healthcare, telecommunications, transportation, military defense, transportation, and more. * 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
Cited By
- Partridge C, De Cesare S, Mitchell A and Odell J (2018). Formalization of the classification pattern, Software and Systems Modeling (SoSyM), 17:1, (167-203), Online publication date: 1-Feb-2018.
- Tolk A and Turnitsa C Conceptual modeling with processes Proceedings of the Winter Simulation Conference, (1-13)
- Reinhard R, Büscher C, Meisen T, Schilberg D and Jeschke S Virtual production intelligence Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part I, (706-715)
- Azizah F, Bakema G, Sitohang B and Santoso O Information grammar for patterns (IGP) for pattern language of data model patterns based on fully communication oriented information modeling (FCO-IM) Proceedings of the 2010 international conference on On the move to meaningful internet systems, (522-531)
Index Terms
- Developing High Quality Data Models
Reviews
Lefteris Angelis
The book is the result of the author's long professional experience in data modeling, and more generally in computers and their business applications. West has worked for more than 20 years on complicated and large-scale problems, having leading roles as a data modeler in large enterprises. The book has three main purposes: help readers understand the usefulness of data models in enterprise architecture (EA); address problems in the development of data models; and transfer knowledge and experience to data modelers on how to develop consistent, generic, and high-quality data models. The style of the book is mostly technical, the level is quite advanced, and the target audience includes professionals, programmers, and software engineers who are familiar with at least the basics of data modeling. It would also be useful for readers to have some background in object-oriented programming, unified modeling language (UML), and data structures. The book's aim is clear: to show how data models can improve the quality of information in decision making. The key notion of "quality"? is defined and used practically as "fit for purpose,"? while the whole data modeling approach is based on ontologies. The book consists of 17 chapters, divided into four parts. Part 1 provides motivations, notations, different uses of data models (especially for EA), and their limitations and challenges. Chapter 1 is a smooth introduction to the motivations and purposes of the book, while chapter 2 presents the fundamental principles and notations for representing data. It also covers the basics of the modeling language EXPRESS, which is used throughout the book. Chapter 3 identifies different types of and uses for data models, and explores the data integration architecture and its requirements and challenges. Chapter 4 connects data models with EA under the general framework of information technology (IT) in business processes. Chapter 5 makes some observations on data models and data modeling, and specifically discusses the models' limitations and challenges in modeling, emphasizing the importance of the ontological approach. Part 2 presents general principles for data models"?especially from the ontological approach"?and their applications to attributes, relationships, and entity types. Chapter 6 starts by discussing the differences between normalization (the elimination of repeated patterns in data) and the ontological approach that is adopted throughout the book. Then, it presents general principles for conceptual, integrated, and enterprise data models, regarding rules, statements, definitions, the naming of entity types, and relationships. Chapters 7 through 9 are devoted to the application of principles to attributes, relationships, and entity types. Part 3 addresses issues related to the ontological framework for developing consistent data models. Chapter 10 offers an interesting overview of the motivations and foundations of an ontological framework using philosophical and logical concepts. Chapter 11 deals with space and time issues, covering spatiotemporal extents; chapter 12 discusses classes under a set theory perspective; and chapter 13 is about intentionally constructed objects, such as agreements and signs. Chapter 14 looks at systems and their components, and chapter 15 considers procurement processes and requirement specifications. Chapter 16 concludes this part with some final remarks. Part 4 provides a full data model, the high-quality data model (HQDM) schema, using both EXPRESS-G diagrams and lexical EXPRESS to describe specifications for various types of objects. Finally, the appendix provides a mapping between the HQDM schema and the ISO 15926-2 model. Overall, the book is a helpful guide for those who wish to go deep into the art of developing high-quality data models. Readers will appreciate how West connects data models with EA and business processes; the ontological approach, which offers a framework for formal, generic, and consistent models; the efficient use of diagrams for explaining the notions; and the philosophical concepts discussed throughout the text. The book is highly technical. Although it does not directly address people from academia, it will be very useful for related courses, especially those that deal with IT and business processes. Finally, the book highlights the importance of quality in data modeling for decision making. Online Computing Reviews Service
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