This book brings all of the elements of database design together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of database design methodology ? from ER and UML techniques, to conceptual data modeling and table transformation, to storing XML and querying moving objects databases.
The proposed book expertly combines the finest database design material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of database design.
This book represents a quick and efficient way to unite valuable content from leading database design experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.
* Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints.
* Details multiple relational models and modeling languages, enhancing the reader's technical expertise and familiarity with design-related requirements specification.
* Coverage of both theory and practice brings all of the elements of database design together in a single volume, saving the reader the time and expense of making multiple purchases.

Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.

People have a hard time communicating, and also have a hard time finding business knowledge in the environment. With the sophistication of search technologies like Google, business people expect to be able to get their questions answered about the business just like you can do an internet search. The truth is, knowledge management is primitive today, and it is due to the fact that we have poor business metadata management.
This book is about all the groundwork necessary for IT to really support the business properly. By providing not just data, but the context behind the data. For the IT professional, it will be tactically practical--very "how to" and a detailed approach to implementing best practices supporting knowledge management. And for the the IT or other manager who needs a guide for creating and justifying projects, it will help provide a strategic map.

* First book that helps businesses capture corporate (human) knowledge and unstructured data, and offer solutions for codifying it for use in IT and management.
* Written by Bill Inmon, one of the fathers of the data warehouse and well-known author, and filled with war stories, examples, and cases from current projects.
* Very practical, includes a complete metadata acquisition methodology and project plan to guide readers every step of the way.
* Includes sample unstructured metadata for use in self-testing and developing skills.

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can't be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You'll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data

Data Warehousing has been around for 20 years and has become part of the information technology infrastructure. Data warehousing originally grew in response to the corporate need for information--not data--and it supplies integrated, granular, and historical data to the corporation.
There are many kinds of data warehouses, in large part due to evolution and different paths of software and hardware vendors. But DW 2.0, defined by this author in many talks, articles, and his b-eye-network newsletter that reaches 65,000 professionals monthly, is the well-identified and defined next generation data warehouse.
The book carries that theme and describes the future of data warehousing that is technologically possible now, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. The benefit of this for people who are building or using a data warehouse can see what lies ahead, and can determine: what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense--at the most practical level.
All of this gives the experienced data warehouse professional everything and exactly what is needed in order to implement the new generation DW 2.0.
* First book on the new generation of data warehouse architecture, DW 2.0.
* Written by the "father of the data warehouse," Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network.
* Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control.

Preventing Litigation, for the first time, explains how to build an early warning system to identify the risk of litigation before the damage is done, and proves that there is big value in less litigation. The authors are subject matter experts, one in litigation, the other in computer science, and each has more than four decades of training and experience in their respective fields. Together, they present a way forward to a transformative revolution for the slow-moving world of law for the benefit of the fast- paced environment of the business world.

Die Client/Server Umgebung ist verf}hrerisch. Anders als in Mainframe-Umgebungen besitzt der Systementwickler hier vollst{ndige Kontrolle }ber Entwicklung und Ausf}hrung der Anwendungen. Da aber Richtlinien und Prinzipien wie in der Gro rechner-Umgebung fehlen, vollziehtsich die System-Planung dabei oft sehr nachl{ssig. Je gr- er die Client/Server Umgebung wird, desto chaotischer kann sie werden. In seinem Buch "Client/Server Anwendungen" definiert W.H. Inmon Richtlinien undPrinzipien der Systementwicklung, die in einer solchen Umgebung gelten sollten - wie sie aussehen, wie man sie implementiert und was geschieht, wenn man sie nicht ber}cksichtigt. Er entwickelt eine Architektur, die auf alle Client/Server Umgebungen }bertragbar ist. Es werden praktische L-sungen angeboten, die dazu verhelfen, duchdachte und stabile Client/Server Anwendungen zu entwickeln. Von ihnen werden Systemprogrammierer ebenso wie Anwender profitieren.