Database Processing
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Query Processing in Database Systems
Author: W. Kim
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
This book is an anthology of the results of research and development in database query processing during the past decade. The relational model of data provided tremendous impetus for research into query processing. Since a relational query does not specify access paths to the stored data, the database management system (DBMS) must provide an intelligent query-processing subsystem which will evaluate a number of potentially efficient strategies for processing the query and select the one that optimizes a given performance measure. The degree of sophistication of this subsystem, often called the optimizer, critically affects the performance of the DBMS. Research into query processing thus started has taken off in several directions during the past decade. The emergence of research into distributed databases has enormously complicated the tasks of the optimizer. In a distributed environment, the database may be partitioned into horizontal or vertical fragments of relations. Replicas of the fragments may be stored in different sites of a network and even migrate to other sites. The measure of performance of a query in a distributed system must include the communication cost between sites. To minimize communication costs for-queries involving multiple relations across multiple sites, optimizers may also have to consider semi-join techniques.
Databases and Transaction Processing
This is a great book! This is the book I wish I had written. --Jim Gray, Microsoft Research, recipient of 1998 A.M. Turing Award for seminal contributions to database and transaction processing researchDatabases and Transaction Processing provides a complete and clear explanation of the conceptual and engineering principles underlying the design and implementation of database and transaction processing applications. Rather than focusing on how to implement the database management system itself, this text focuses on how to build database applications. To provide a solid foundation for these principles, the book thoroughly covers the theory underlying relational databases and relational query languages.To illustrate both database and transaction processing concepts, a case study is carried throughout the book. The technical aspects of each chapter applied to the case study and the software engineering concepts required to implement the case study are discussed.In addition to the more traditional material -- relational databases, SQL, and the ACID properties of transactions -- the book provides in-depth coverage of the most current topics in database and transaction processing tec
Big Data Management and Processing
From the Foreword: "Big Data Management and Processing is [a] state-of-the-art book that deals with a wide range of topical themes in the field of Big Data. The book, which probes many issues related to this exciting and rapidly growing field, covers processing, management, analytics, and applications... [It] is a very valuable addition to the literature. It will serve as a source of up-to-date research in this continuously developing area. The book also provides an opportunity for researchers to explore the use of advanced computing technologies and their impact on enhancing our capabilities to conduct more sophisticated studies." ---Sartaj Sahni, University of Florida, USA "Big Data Management and Processing covers the latest Big Data research results in processing, analytics, management and applications. Both fundamental insights and representative applications are provided. This book is a timely and valuable resource for students, researchers and seasoned practitioners in Big Data fields. --Hai Jin, Huazhong University of Science and Technology, China Big Data Management and Processing explores a range of big data related issues and their impact on the design of new computing systems. The twenty-one chapters were carefully selected and feature contributions from several outstanding researchers. The book endeavors to strike a balance between theoretical and practical coverage of innovative problem solving techniques for a range of platforms. It serves as a repository of paradigms, technologies, and applications that target different facets of big data computing systems. The first part of the book explores energy and resource management issues, as well as legal compliance and quality management for Big Data. It covers In-Memory computing and In-Memory data grids, as well as co-scheduling for high performance computing applications. The second part of the book includes comprehensive coverage of Hadoop and Spark, along with security, privacy, and trust challenges and solutions. The latter part of the book covers mining and clustering in Big Data, and includes applications in genomics, hospital big data processing, and vehicular cloud computing. The book also analyzes funding for Big Data projects.