For a few decades, structured data are typically arranged as relational tables and stored in relational databases. Recent years have witnessed the blossom of graph databases, for which graph becomes an alternative to model structured data.
In this tutorial, we will give an overview of the recent advances in the fusion of relational and graph database techniques. In particular, we mainly focus on the key operations (i.e., relational join and subgraph matching) for both types of databases, a recent query processing techniques to answer relational queries against a graph database and graph queries against a relational database. We will also conduct a brief review of the development history of graph databases and point out promising directions for future research.
The tutorial is planned for 1.5 hours and will have the following structure.
- Introduction and Motivation (10 minutes). We briefly review the history of relational databases (RDBMSs) and the recent advance of graph databases (GDBMSs). Then, for the management of structured data, we propose the motivation of a unified perspective by combining relational and graph database techniques.
- Multi-Model Queries (20 minutes). We first focus on the query languages and briefly introduce several languages for querying multi-model data. In particular, we put emphasis on their essential semantics for querying structured data. We also discuss the corresponding optimizations.
- Key Operations: Join and Subgraph Matching (15 minutes). We present different types of join algorithms including binary joins and worst-case optimal joins, as well as the taxonomy of subgraph matching algorithms. We also discuss their equivalence.
- Fusion of Query Processing Techniques (40 minutes)}. We introduce relational database techniques for answering graph queries where data are arranged as relational tables, and graph database techniques for answering relational queries, in which we store structured data as graphs. For both cases, we present representative approaches and key techniques.
- Open Challenges (5 minutes). We discuss open problems and challenges for the fusion of relation and graph databases.
Speakers:
- Dr. Yu Liu is a Lecturer at Beijing Jiaotong University, China. His current research interests include key techniques for graph databases and multi-model databases such as scalable graph algorithms and graph-based learning. He has published more than 10 refereed papers in various journals and conferences.
- Dr. Qingsong Guo is a Lecturer at the School of Computer Science and Technology (School of Data Science), North University of China (NUC). His research interests include multi-model data management and automatic management of big data with deep learning algorithms.
- Dr. Jiaheng Lu is a Professor at the University of Helsinki. His main research interests lie in big data management and database systems. He has written four books on Hadoop and NoSQL databases, and more than 100 journal and conference papers published in SIGMOD, VLDB, TODS, TKDE, etc.