Nowadays, large open datasets are frequently accessed to select, for example, restaurants that best meet gastronomy criteria and are closer to their current geo-spatial locations. We have developed a skyline-based ranking approach named FOPA, which is able to efficiently rank resources that fullfil this type of multi-objective queries. As a proof of concept, we developed FRAGOLA (Fabulous RAnking of GastrOnomy LocAtions), a tool that implements FOPA and ranks gastronomy locations based on multi-objective criteria. We will demonstrate FRAGOLA, and attendees will observe scenarios where FOPA overcomes performance of existing skyline-based approaches by up to two orders of magnitude.
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