Storing and Querying XML:An Efficient Approach Using Relational Databases Mustafa Atay
Efficient Approaches for Querying XML Data:Keyword-Based, Personalized, and Distributed Queries Kamal Taha
Storing and Querying Large XML Instances:Design of a full-fledged XML storage and query architecture Christian Grün
Quickly find solutions to dozens of common problems encountered while using XML and JSON features that are built into SQL Server. Content is presented in the popular problem-solution format. Look up the problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! This book shows how to take advantage of XML and JSON to share data and automate tasks. JSON is commonly used to move data back and forth between the database and front-end applications, often running in a browser. This book shows all you need to know about transforming query results into JSON format, and back again. Also covered are the processes and techniques for moving data into and out of XML format for business intelligence and other purposes, such as when transferring data from a reporting system into a data warehouse, or between different database brands such as between SQL Server and Oracle. Microsoft intensively implements XML in SQL Server, and in many related products. Execution plans are generated in XML format, and this book shows you how to parse those plans and automate the detection of performance problems. The relatively new Extended Events feature writes tracing data into XML files, and the recipes in this book help in parsing those files. XML is also used in SQL Server´s BI tool set, including in SSIS, SSR, and SSAS. XML is used in many configuration files, and is even behind the construction of DDL triggers. In reading this book you´ll dive deeply into the features that allow you to build and parse XML, and also JSON, which is a specific format of XML used to transmit objects in a web-friendly format between a database and its front-end applications. What You Will Learn Build XML and JSON objects in support of automation and data transfer Import and parse XML and JSON from operating system files Build appropriate indexes on XML objects to improve query performance Move data from query result sets into JSON format, and back again Automate the detection of database performance problems by querying and parsing the database´s own execution plans Replace external and manual JSON processes with SQL Server´s internal, JSON functionality Who This Book Is For Database administrators, .NET developers, business intelligence developers, and other professionals who want a deep and detailed skill set around working with XML and JSON in a SQL Server database environment. Web developers will particularly find the book useful for its coverage of transforming database result sets into JSON text that can be transmitted to front-end web applications.
Interested in how an efficient search engine works? Want to know what algorithms are used to rank resulting documents in response to user requests? The authors answer these and other key information retrieval design and implementation questions. This book is not yet another high level text. Instead, algorithms are thoroughly described, making this book ideally suited for both computer science students and practitioners who work on search-related applications. As stated in the foreword, this book provides a current, broad, and detailed overview of the field and is the only one that does so. Examples are used throughout to illustrate the algorithms. The authors explain how a query is ranked against a document collection using either a single or a combination of retrieval strategies, and how an assortment of utilities are integrated into the query processing scheme to improve these rankings. Methods for building and compressing text indexes, querying and retrieving documents in multiple languages, and using parallel or distributed processing to expedite the search are likewise described. This edition is a major expansion of the one published in 1998. Besides updating the entire book with current techniques, it includes new sections on language models, cross-language information retrieval, peer-to-peer processing, XML search, mediators, and duplicate document detection.
Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions.This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market.