Browsing by Author "Concas, Francesco"

The Bloom Filter is a space-efficient probabilistic data structure that deals with the problem of set membership. The space reduction comes at the expense of introducing a false positive rate that many applications can tolerate since they require approximate answers. In this thesis, we extend the Bloom Filter to deal with the problem of matching multiple labels to a set, introducing two new data structures: the Bloom Vector and the Bloom Matrix. We also introduce a more efficient variation for each of them, namely the Optimised Bloom Vector and the Sparse Bloom Matrix. We implement them and show experimental results from testing with artificial datasets and a real dataset.