Big Data Management Large-Scale Graph Analysis: System, Algorithm and Optimization, (Hardcover)

<p>This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms - the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms.</p> <p>This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.</p><p></p><p></p>

• Big Data Management Large-Scale Graph Analysis: System, Algorithm and Optimization, (Hardcover)
• Author: Springer
• ISBN: 9789811539275
• Format: Hardcover
• Publication Date: 2020-07-02
• Page Count: 146