Konferenzbeitrag
| Autoren | Daniel Glake, Mareike Schmidt, Felix Kiehn, Fabian Panse, Ulfia Lenfers, Thomas Clemen, Norbert Ritter |
| Titel | Operator Placement for Spatio-temporal Tasks |
| Publiziert in | 2022 IEEE International Conference on Big Data (Big Data) |
| Adresse | Osaka, Japan |
| Datum | 2022 |
| Seiten | 281-290 |
| URL | http://dx.doi.org/10.1109/BigData55660.2022.10020279 |
| Zusammenfassung | 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 processing a cross different systems is 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. |
| Dokument |  |
| Andere Formate | Din 1501 |
