Amol Deshpande

In an era of increasingly interconnected information, graph-structured data has become pervasive across numerous domains, from social media platforms and telecommunication networks to biological systems and knowledge graphs. However, traditional database management systems often struggle when confronted with the unique challenges posed by graph-structured data, in large part due to the explosion of intermediate results, the complexity of join-heavy queries, and the use of regular path queries. This monograph provides a comprehensive overview of modern query processing techniques designed to address these challenges. Four key components that have emerged as pivotal in optimizing queries on graph-structured databases are focused on, namely: (1) Predefined joins, which leverage precomputed data structures to accelerate joins; (2) Worst-case optimal join algorithms, that avoid redundant computations for queries with cycles; (3) Factorized representations, which compress intermediate and final query results; and (4) Advanced techniques for processing recursive queries, essential for traversing graph structures. For each component, theoretical underpinnings are covered and design considerations are explored, and implementation challenges associated with integrating these techniques into existing database management systems are discussed. This monograph aims to serve as a comprehensive resource for both researchers pushing the boundaries of query processing and practitioners seeking to implement state-of-the-art techniques, in addition to offering insights into future research directions in this rapidly evolving field.

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.