A Semantics-Enabled Approach for Data Lake Exploration Services

Ignited by the advent of Data Science, organisations are spending more and more resources in understanding their Big Data, attracted by the opportunity of turning them into actionable insights. Data Lakes have been proposed as repositories in charge of storing vast amount of heterogeneous data, regardless its structure, enabling the possibility of postponing transformation and analytical processes. In this context, Semantic Web technologies may be used to enable interoperability and improve data access, by providing Data Exploration Services. Starting from these premises, the goal of this paper is to describe a semantic approach apt to the compelling challenge of Data Exploration Services, aimed at personalising the exploration experience. The approach has been preliminary validated within a Smart City context, where aggregation of urban data, according to multiple perspectives through the definition of proper indicators, enables urban data exploration at different granularity levels for distinct categories of users.