Publication at Faculty of Mathematics and Physics |
2022
Abstract
Multi-model data is organised in various mutually interlinked formats and models, often with contradictory features. In addition, its structure may change over time, and its size can grow to the extremes of Big Data. In terms of research and practical processing, this creates one of the most complex challenges of effective data management.
As it is not humanly possible to handle such a complex task manually, in this vision paper, we focus on the area of automatic management of dynamic multi-model Big Data. We envision a framework capable of accepting different levels of user input and different types of data, queries, changes, and propagation strategies and ensuring the preservation of adequate and efficient data access.