Orateur
Description
The data explosion in astronomy requires the development of new techniques both from the infrastructure and from the analysis side. In particular, the increase of the data complexity demands a parallel effort to deliver efficient and standardized solutions for accessing and managing data, tools and software. This is the main purpose of ESCAPE. In this talk I will give an overview of the work fulfilled within the project, presenting MEGAVIS, a prototype based on machine learning, which aims to start building a new paradigm for data access and search, not based on explicit criteria but implicitly, looking at similarities. The prototype is based on dimensionality reduction models, and in particular on an autoencoder. The main features and capabilities of the software will be illustrated, and the possibilities of future developments.
