Performance Evaluation of Bloom Multifilters

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dc.contributor Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta, Tietojenkäsittelytieteen laitos fi
dc.contributor University of Helsinki, Faculty of Science, Department of Computer Science en
dc.contributor Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap sv Concas, Francesco 2018
dc.identifier.uri URN:NBN:fi:hulib-201804131683
dc.description.abstract 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. en
dc.language.iso eng
dc.publisher Helsingin yliopisto fi
dc.publisher University of Helsinki en
dc.publisher Helsingfors universitet sv
dc.title Performance Evaluation of Bloom Multifilters en
dc.type.ontasot pro gradu -tutkielmat fi
dc.type.ontasot master's thesis en
dc.type.ontasot pro gradu-avhandlingar sv
dc.subject.discipline Computer science en
dc.subject.discipline Tietojenkäsittelytiede fi
dc.subject.discipline Datavetenskap sv
dct.identifier.urn URN:NBN:fi:hulib-201804131683

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