Foundations and Trends (R) in Databases
2 total works
Big Graph Analytics Platforms
by Da Yan, Bu Yingyi, Yuanyuan Tian, and Amol Deshpande
Published 12 January 2017
The growing need to deal with massive graphs in real-life applications has led to a surge in the development of big graph analytics platforms. Tens of such big graph systems have already been developed, and more are expected to emerge in the near future.
Although several experimental studies have been conducted in recent years that compare the performance of several big graph systems, Big Graph Analytics Platforms is the first text to provide a comprehensive survey that clearly summarizes the key features and techniques developed in existing systems.
It aims to help readers get a systematic picture of the landscape of recent big graph systems, focusing not just on the systems themselves, but also on the key innovations and design philosophies underlying them. In addition to the popular vertex-centric systems which espouse a think-like-a-vertex paradigm for developing parallel graph applications, Big Graph Analytics Platforms also covers other programming and computation models, contrasts those against each other, and provides a vision for future research in the field.
Although several experimental studies have been conducted in recent years that compare the performance of several big graph systems, Big Graph Analytics Platforms is the first text to provide a comprehensive survey that clearly summarizes the key features and techniques developed in existing systems.
It aims to help readers get a systematic picture of the landscape of recent big graph systems, focusing not just on the systems themselves, but also on the key innovations and design philosophies underlying them. In addition to the popular vertex-centric systems which espouse a think-like-a-vertex paradigm for developing parallel graph applications, Big Graph Analytics Platforms also covers other programming and computation models, contrasts those against each other, and provides a vision for future research in the field.
Adaptive Query Processing
by Amol Deshpande, Zachary Ives, and Vijayshankar Raman
Published 7 August 2007
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. It 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.
The authors identify the strengths and limitations of the different techniques, demonstrate when they are most useful, and suggest possible avenues of future research. It 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.