This book celebrates Michael Stonebraker's accomplishments that led to his 2014 ACM A.M. Turing Award "for fundamental contributions to the concepts and practices underlying modern database systems." The book describes, for the broad computing community, the unique nature, significance, and impact of Mike's achievements in advancing modern database systems over more than forty years. Today, data is considered the world's most valuable resource, whether it is in the tens of millions of databases used to manage the world's businesses and governments, in the billions of databases in our smartphones and watches, or residing elsewhere, as yet unmanaged, awaiting the elusive next generation of database systems. Every one of the millions or billions of databases includes features that are celebrated by the 2014 Turing Award and are described in this book. Why should I care about databases? What is a database? What is data management? What is a database management system (DBMS)? These are just some of the questions that this book answers, in describing the development of data management through the achievements of Mike Stonebraker and his over 200 collaborators. In reading the stories in this book, you will discover core data management concepts that were developed over the two greatest eras (so far) of data management technology. The book is a collection of 36 stories written by Mike and 38 of his collaborators: 23 world-leading database researchers, 11 world-class systems engineers, and 4 business partners. If you are an aspiring researcher, engineer, or entrepreneur you might read these stories to find these turning points as practice to tilt at your own computer-science windmills, to spur yourself to your next step of innovation and achievement.

After a long period of research, development, test and trial, relational database management systems are at last being marketed in force. The feedback from early installations of these systems is overwhelmingly positive. The most frequent comment by users is that productivity has been increased by a significant factor (from 5 to 20 times what it was using previous approaches). Another comment is that, in many cases, end users can now handle their own problems by direct use of the system instead of using application programmers as mediators between them and the system. As the reputation of relational systems for ease of use and enhanced productivity has grown, there has been a strong temptation for vendors of other approaches to exploit the label "relational" somewhat indiscriminately. In some cases the label is being misapplied to a whole data system; in others it is being misapplied to an interface. It is therefore worth developing criteria which database management systems (DBMSs) should have in order to be called "relational." The Relational Task Group (RTG) of the American National Standards Institute (ANSI) undertook such an effort by developing a characterization of RDBMSs and analyzing fourteen DBMSs per this characterization. The result of this work is presented in this book. The conclusions of the RTG are in agreement with my view that a DBMS should not be called "relational" unless it satisfies at least the following conditions: 1. All information in the database is represented as values in tables.

Current experimental systems in industry, government, and the military take advantage of knowledge-based processing. For example, the Defense Advanced Research Projects Agency (DARPA), and the United States Geological Survey (USGS) are supporting the develop ment of information systems that contain diverse, vast, and growing repositories of data (e.g., vast databases storing geographic informa tion). These systems require powerful reasoning capabilities and pro cessing such as data processing, communications, and multidisciplinary of such systems will scientific analysis. The number and importance grow significantly in the near future. Many of these systems are severely limited by current knowledge base and database systems technology. Currently, knowledge-based system technology lacks the means to provide efficient and robust knowledge bases, while database system technology lacks knowledge representation and reasoning capabilities. The time has come to face the complex research problems that must be solved before we can design and implement real, large scale software systems that depend on knowledge-based processing. To date there has been little research directed at integrating knowledge base and database technologies. It is now imperative that such coordinated research be initiated and that it respond to the urgent need for a tech nology that will enable operational large-scale knowledge-based system applications."