The amount of publicly available Spatio-temporal (ST) data is growing daily and possesses an increasing degree of complexity in more and more use cases. Besides spatial queries such as intersection, the requirements of current applications like Digital Twins (DT) go beyond the limits of a single data processing platform and need to combine a variety of queries with filtering ( e.g., k -NN), aggregation (e.g., counting), ranking (e.g., page-rank), clustering (e.g., k-means, ST-DBSCAN) and more, on ST-models. Since existing ST-platforms are highly specialized for a subset of these operations, it seems logical to distribute the data and queries across several of these systems. However, efficient p rocessing a cross d ifferent s ystems i s still a major challenge in polyglot data management and often demands manual query planning. To solve the automatic planning of those complex queries, we present an approach for cross-platform processing of ST-tasks that uses a symmetric join to handle platform heterogeneity and includes a novel algorithm for operator placement based on a latency model. Although the underlying problem is NP-hard and additional network transfers slow down the overall processing time, experiments on real-world tasks for DTs have shown that cross-platform processing can speed up well-known ST-tasks compared to the expensive query reformulations performed by state-of-the-art ST single-platform solutions.

Due to the increasing variety of the current database landscape, polyglot data management has become a hot research topic in recent years. The underlying idea is to combine the benefits of different data stores behind a predefined set of common interfaces and thus address use cases that individual stores cannot meet. This can be accomplished using different approaches which vary greatly in terms of capabilities, functionality, and architectural concepts. This tutorial provides a detailed overview of the current state of research in polyglot data management. We motivate its use by showing the high diversity of existing data stores and discussing three use cases in which individual stores are insufficient. Thereafter, we present different taxonomies for classifying polyglot data systems and give a detailed review of a number of selected systems. Finally, we compare these systems based on their features and discuss open challenges that still need to be addressed in future research.