A relevant kind of task, which is getting more and more attention in the recent years, is the selection of the most relevant elements in a number of data collections, e.g., the opinion leaders given a set of topics, the best hotel offers, and the best cities to live in. At the moment the problem is addressed through ad-hoc solutions tailored on the target scenario, setting up infrastructures able to manage the expected data loads. Even if these solutions work, they could start to have problems when the data volume, velocity and variety increase. In my research activity I will study the problem of computing the top k relevant items given a collection of data sets with both streaming and static data, an ontology describing them, and a set of top-k queries where each scoring function describes the relevance as a combination of several criteria.
["Daniele Dell'Aglio"]
Ontology-based top-k query answering over massive, heterogeneous, and dynamic data