Many databases today capture both, structured and unstructured data. Making use of such hybrid data has become an important topic in research and industry. The efficient evaluation of hybrid data queries is the main topic of this thesis. Novel techniques are proposed that improve the whole processing pipeline, from indexes and query optimization to run-time processing. The contributions are evaluated in extensive experiments showing that the proposed techniques improve upon the state of the art.

Ranking - the algorithmic decision on how relevant an information artifact is for a given information need and the sorting of artifacts by their concluded relevancy - is an integral part of every search engine. In this book we investigate how structured Web data can be leveraged for ranking with the goal to improve the effectiveness of search. We propose new solutions for ranking using on-the-fly data integration and experimentally analyze and evaluate them against the latest baselines.

This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs.

This book provides basic knowledge about main memory management in relational databases as it is needed to support large-scale applications processed completely in memory. In business operations, real-time predictability and high speed is a must. Hence every opportunity must be exploited to improve performance, including reducing dependency on the hard disk, adding more memory to make more data resident in the memory, and even deploying an in-memory system where all data can be kept in memory. The book provides one chapter for each of the main related topics, i.e. the memory system, memory management, virtual memory, and databases and their memory systems, and it is complemented by a short survey of six commercial systems: TimesTen, MySQL, VoltDB, Hekaton, HyPer/ScyPer, and SAP HANA.

"Advanced SQL Queries: Writing Efficient Code for Big Data" is an essential guide for data professionals seeking to deepen their expertise in SQL amidst the complexities of Big Data environments. This comprehensive book navigates the intricacies of advanced SQL techniques and performance optimization, equipping readers with the skills needed to manage and analyze vast datasets effectively. From learning to write complex queries and mastering data warehousing techniques to exploring SQL's integration in NoSQL environments, the book provides a detailed roadmap to harnessing the full potential of SQL in data-intensive scenarios. Through a structured approach, this book delves into the evolving landscape of SQL, addressing contemporary challenges such as real-time data management, security, and data governance. It also sheds light on future trends, including the interplay of AI and machine learning with SQL, ensuring that readers stay ahead of technological shifts. Suitable for both emerging data scientists and experienced database administrators, "Advanced SQL Queries" serves as a vital resource to elevate one’s proficiency, enabling professionals to drive data-driven insights and decisions with confidence and precision.

The two-volume set LNCS 13141 and LNCS 13142 constitutes the proceedings of the 28th International Conference on MultiMedia Modeling, MMM 2022, which took place in Phu Quoc, Vietnam, during June 6–10, 2022. The 107 papers presented in these proceedings were carefully reviewed and selected from a total of 212 submissions. They focus on topics related to multimedia content analysis; multimedia signal processing and communications; and multimedia applications and services.

Adaptive Query Processing surveys the fundamental issues, techniques, costs, and benefits of adaptive query processing. It begins with a broad overview of the field, identifying the dimensions of adaptive techniques. It then looks at the spectrum of approaches available to adapt query execution at runtime - primarily in a non-streaming context. The emphasis is on simplifying and abstracting the key concepts of each technique, rather than reproducing the full details available in the papers. The authors identify the strengths and limitations of the different techniques, demonstrate when they are most useful, and suggest possible avenues of future research. Adaptive Query Processing serves as a valuable reference for students of databases, providing a thorough survey of the area. Database researchers will benefit from a more complete point of view, including a number of approaches which they may not have focused on within the scope of their own research.