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dc.contributor.authorArolfo, Franco A.
dc.contributor.authorVaisman, Alejandro Ariel
dc.date.accessioned2019-06-14T19:05:27Z
dc.date.available2019-06-14T19:05:27Z
dc.date.issued2018
dc.identifier.isbn978-331998397-4
dc.identifier.issn0302-9743
dc.identifier.urihttp://ri.itba.edu.ar/handle/123456789/1615
dc.description.abstract"In each of the phases of a Big Data analysis process, data quality (DQ) plays a key role. Given the particular characteristics of the data at hand, the traditional DQ methods used for relational databases, based on quality dimensions and metrics, must be adapted and extended, in order to capture the new characteristics that Big Data introduces. This paper dives into this problem, re-defining the DQ dimensions and metrics for a Big Data scenario, where data may arrive, for example, as unstructured documents in real time. This general scenario is instantiated to study the concrete case of Twitter feeds. Further, the paper also describes the implementation of a system that acquires tweets in real time, and computes the quality of each tweet, applying the quality metrics that are defined formally in the paper. The implementation includes a web user interface that allows filtering the tweets for example by keywords, and visualizing the quality of a data stream in many different ways. Experiments are performed and their results discussed."en
dc.language.isoenen
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-98398-1_11
dc.relationinfo:eu-repo/grantAgreement/FONCyT/PICT/2014 0787/AR. Ciudad Autónoma de Buenos Aires
dc.subjectCALIDAD DE DATOSes
dc.subjectANALISIS DE DATOSes
dc.subjectREDES SOCIALESes
dc.titleData quality in a big data contexten
dc.typeArtículos de Publicaciones Periódicases
dc.typeinfo:eu-repo/semantics/acceptedVersion
itba.description.filiationFil: Arolfo, Franco A. Instituto Tecnológico de Buenos Aires; Argentina.es
itba.description.filiationFil: Vaisman, Alejandro Ariel. Instituto Tecnológico de Buenos Aires; Argentina.es


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