In 1887, Tip O'Neill, left fielder for the St. Louis Browns, won the American Association batting championship with a .492 average--the highest ever for a single season in the Major Leagues. Yet his record was set during a season when a base on balls counted as a hit and a time at bat. Over the next 130 years, the debate about O'Neill's "correct" average diverted attention from the other batting feats of his record-breaking season, including numerous multi-hit games, streaks and long hits, as well as two cycles and the triple crown. The Browns entered 1887 as the champions of St. Louis, the American Association and the world. Following the lead set by their manager, Charles Comiskey, the Browns did "anything to win," combining skill with an aggressive style of play that included noisy coaching, incessant kicking, trickery and rough play. O'Neill did "everything to win" at the plate, leaving the no-holds-barred tactics to his rowdier teammates.

Database Modeling and Design, Fifth Edition, focuses on techniques for database design in relational database systems. This extensively revised fifth edition features clear explanations, lots of terrific examples and an illustrative case, and practical advice, with design rules that are applicable to any SQL-based system. The common examples are based on real-life experiences and have been thoroughly class-tested. This book is immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data. It is ideal for a stand-alone data management course focused on logical database design, or a supplement to an introductory text for introductory database management. In-depth detail and plenty of real-world, practical examples throughout Loaded with design rules and illustrative case studies that are applicable to any SQL, UML, or XML-based system Immediately useful to anyone tasked with the creation of data models for the integration of large-scale enterprise data

Health and welfare issues of brachycephalic (flat-faced) animals are one of the most pressing problems facing companion animals right now. Dogs, in particular, are suffering from a ‘brachycephalic crisis’ resulting from a perfect storm where predispositions to an array of health issues are amplified by a population boom for certain brachycephalic breeds such as the French Bulldog and Pug. But yet, for many owners, these dogs represent the perfect companion: endearing personas and cute looks in a socially desirable package. So where is the truth in all of this? This book will equip veterinary professionals, animal welfare scientists, breeders and owners with the fuller story about brachycephalic health and welfare. The first half of the book provides the context of how and why we are in this crisis, offering in-depth historical, social, ethical, communication, nursing, welfare, epidemiological, genetics and international perspectives. The second half shifts towards the clinical arena, with chapters that cover the background, diagnosis, treatment and prevention of the many unique healthcare needs of brachycephalic animals. Cutting-edge knowledge is shared on a range of disciplines including respiratory disease, ophthalmology, dermatology, dentistry, neurology, obesity, reproduction and anesthesia. With twenty chapters written by world-leading experts, lifetimes of experience and knowledge are condensed into the first book dedicated exclusively to brachycephaly in companion animals. This essential reference resource will inform, challenge and stimulate; it will open your mind to new opportunities for you to improve the welfare of brachycephalic animals by your personal and collective choices and actions. But prepare to be surprised: you may just find that your views on brachycephaly in companion animals will be changed forever.

Magazines, journals, newspapers, bulletins, statistical annuals, loose leaf business services, governmental agency annual reports, directories, proceedings of annual conferences, yearbooks.

This book examines the field of parallel database management systems and illustrates the great variety of solutions based on a shared-storage or a shared-nothing architecture. Constantly dropping memory prices and the desire to operate with low-latency responses on large sets of data paved the way for main memory-based parallel database management systems. However, this area is currently dominated by the shared-nothing approach in order to preserve the in-memory performance advantage by processing data locally on each server. The main argument this book makes is that such an unilateral development will cease due to the combination of the following three trends: a) Today’s network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory on a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic and provide high-availability. c) A modern storage system such as Stanford’s RAM Cloud even keeps all data resident in the main memory. Exploiting these characteristics in the context of a main memory-based parallel database management system is desirable. The book demonstrates that the advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible.

The SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS). FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.