Séminaire AtlanMod le jeudi 10 novembre
Thursday 10th November, AtlanMod team organizes a seminar (10:00-11:30, amphi Gallois@EMN). The seminar is open to anybody.
The presentations will be done by Adel Ferdjoukh and Gwendal Daniel.
Adel is a new ATER in the team. He will present the work done during his thesis, that he defended a few days ago.
Gwendal is preparing a PhD. He will present a paper which received a Best Paper Award at Models16, a few weeks ago.
Here are the titles and abstract of those talks :
A declarative approach for generation of valid, realistic and diverse models
Owning models is useful in many different fields. Models can be used to test and to validate approaches, algorithms and concepts. Unfortunately, models are rarely available, are cost to obtain, or are not adapted to most of cases due to a lack of quality.
An automated model generator is a good way to generate quickly and easily models that are valid, in different sizes, likelihood and diverse.
In this presentation, we propose a novel and declarative model driven approach, based on constraint programming for automated model generation. Models are modelled as a meta-model. This later is then encoded in constraint programming to find solutions. For more useful models, we are concerned by their likelihood, which is obtained by simulating probability distributions related to domain-specific metrics and also by their diversity through model comparison distances.
PrefetchML : a Framework for Prefetching and Caching Models
Prefetching and caching are well-known techniques integrated in database engines and file systems in order to speed-up data access. They have been studied for decades and have proven their efficiency to improve the performance of I/O intensive applications. Existing solutions do not fit well with scalable model persistence frameworks because the prefetcher operates at the data level, ignoring potential optimizations based on the information available at the metamodel level. Furthermore, prefetching components are common in relational databases but typically missing (or rather limited) in NoSQL databases, a common option for model storage nowadays. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to precisely configure the prefetching rules to follow. Our experiments show that PrefetchML provides a significant execution time speedup. Tool support is fully available online.