Title : And now for something completely different: modeling farmer behavior
| Presenter | Mark Hoogendoorn |
| Abstract | A student I supervised did an internship at Wageningen University, and this resulted in a paper in the farming domain, which is pretty far from my typical research theme :) But it might be interesting thought. The abstract of the paper is as follows: Gaining insight on the effect of policies upon the Agricultural domain is essential to effectively regulate such a sector. A variety of models have therefore been developed that enable a prediction of agricultural development under different policies. Most models do however not make predictions on a fine grained level, making it difficult to see the real effect for individual farmers. This paper presents an agent-based model where each farm is modeled by means of an agent and studies the effect of milk quota abolishment. Simulations are performed for the Netherlands and compared with the predictions of more coarse grained models. |
Title : RDF Reasoning Optimization and Predictability Improvement for real-world usages
| Presenter | Hamid Bazoobandi |
| Abstract | Poor performance, and non-predictability have long been the challenges of reasoning on RDF data. Therefore, vast studies have been conducted to mitigate these problems by improving the reasoning algorithms. Most existing efforts though useful are incomplete, because 1) they attempt to address the problem for the worst-case scenario and in very high level, 2) they do not consider the effect of underlying computer system on behavior of reasoner as a computer program. Given the fact that the worst case happens very infrequently and the overhead of underlying computer system is not negligible for programs with special demands like a reasoner, we believe there is still a great chance of improvement for RDF reasoners. To that end, we advocate a different approach which devises adaptations to reasoners for better performance and more predictability based on observations of reasoning on real-world data. Our goal is to leverage the characteristics of input data as well as spotting and resolving the system bottlenecks to boost the performance and predictability of RDF reasoners. |