Tutorial on multi-model data management

multi-model database

Abstract

As more businesses realized that data, in all forms and sizes, is critical to making the best possible decisions, we see the continued growth of systems that support massive volume of non-relational or unstructured forms of data. Nothing shows the picture more starkly than the Gartner Magic quadrant for operational database management systems, which assumes that, by 2017, all leading operational DBMSs will offer multiple data models, relational and NoSQL, in a single DBMS platform. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach reduces significantly integration, migration, development, maintenance, and operational issues. Therefore, a challenging research work is how to develop efficient consolidated single data management platform covering both relational data and NoSQL to reduce integration issues, simplify operations, and eliminate migration issues.

In this tutorial, we review the previous work on multi-model data management and provide the insights on the research challenges and directions for future work.

Authors: Jiaheng Lu and Irena Holubova

Tutorial in Proc. 20th International Conference on Extending Database Technology (EDBT), March 21-24, 2017.

See more details on this tutorial: [PDF]

Presentation slides of this tutorial: [Slides]

Related papers with this turotial:

  • Jiaheng Lu: Towards Benchmarking Multi-Model Databases(Abstract) CIDR 2017 (Download PDF)

  • Jiaheng Lu, Zhen Hua Liu, Pengfei Xu, Chao Zhang: UDBMS: Road to Unification for Multi-model Data Management. CoRR abs/1612.08050 (2016) (Download PDF)
Open multi-model datasets
UniBench: Towards Benchmarking Multi-Model DBMS