There exist various standards for different models of data, and hence users often must handle a zoo of data models. Storing and processing data in their native models, but spanning optimizations and processing across these models seem to be the most efficient way, such that we recently observe an advent of multi-model databases for this purpose. Companies, end users and developers typically run different platforms like mobile devices, web, desktops, servers, clouds and post-clouds (e.g., fog and edge computing) as execution environments for their applications at the same time. In this paper, we propose to utilize the different platforms according to their advantages and benefits for data distribution, query processing and transaction handling in an overall integrated hybrid multi-model multi-platform (HM3P) database. We analyze current state-of-the-art multi-model databases according to the support of multiple platforms. Furthermore, we analyze the properties of databases running on different types of platforms. We detail new challenges for the novel concept of HM3P databases concerning a global optimization of data distribution, query processing and transaction handling across multiple platforms.

Original languageEnglish
Title of host publicationDATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications
Number of pages8
Publication date07.2020
ISBN (Print)978-989758440-4
Publication statusPublished - 07.2020
Event9th International Conference on Data Science - Online Streaming (Virtual, Online)
Duration: 07.07.202009.07.2020
Conference number: 162152

Research Areas and Centers

  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)
  • Research Area: Intelligent Systems

DFG Research Classification Scheme

  • 409-04 Operating, Communication, Database and Distributed Systems


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