Abstract
Providing decision support for questions such as 'Which Patient to Treat Next?' requires a combination of stream-based reasoning and probabilistic reasoning. The former arises due to a multitude of sensors constantly collecting data (data streams). The latter stems from the underlying decision making problem based on a probabilistic model of the scenario at hand. The STARQL engine handles temporal data streams efficiently and the lifted dynamic junction tree algorithm handles temporal probabilistic relational data efficiently. In this paper, we leverage the two approaches and propose probabilistic stream-based reasoning. Additionally, we demonstrate that our proposed solution runs in linear time w.r.t. the maximum number of time steps to allow for real-time decision support and monitoring.
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE International Conference on Big Knowledge (ICBK) |
| Number of pages | 8 |
| Publisher | IEEE |
| Publication date | 11.2019 |
| Pages | 73-80 |
| Article number | 8944691 |
| ISBN (Print) | 978-1-7281-4608-9 |
| ISBN (Electronic) | 978-1-7281-4607-2 |
| DOIs | |
| Publication status | Published - 11.2019 |
| Event | 10th IEEE International Conference on Big Knowledge - Beijing, China Duration: 10.11.2019 → 11.11.2019 Conference number: 156494 |
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
