Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Crescenzi, Valter"'
Autor:
Crescenzi, Valter, De Angelis, Andrea, Firmani, Donatella, Mazzei, Maurizio, Merialdo, Paolo, Piai, Federico, Srivastava, Divesh
Data integration is a long-standing interest of the data management community and has many disparate applications, including business, science and government. We have recently witnessed impressive results in specific data integration tasks, such as E
Externí odkaz:
http://arxiv.org/abs/2101.11259
In this paper, we present a meta-analysis of several Web content extraction algorithms, and make recommendations for the future of content extraction on the Web. First, we find that nearly all Web content extractors do not consider a very large, and
Externí odkaz:
http://arxiv.org/abs/1508.04066
Autor:
Crescenzi, Valter1 crescenz@dia.uniroma3.it, Mecca, Giansalvatore2 mecca@unibas.it
Publikováno v:
Journal of the ACM. Sep2004, Vol. 51 Issue 5, p731-779. 49p. 1 Black and White Photograph, 17 Diagrams, 2 Charts.
Akademický článek
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Publikováno v:
In Data & Knowledge Engineering 2005 54(3):279-299
Akademický článek
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Publikováno v:
Crescenzi, V, Fernandes, A A A, Merialdo, P & Paton, N W 2017, ' Crowdsourcing for Data Management: a Survey ', Knowledge and Information Systems, pp. 1-41 . https://doi.org/10.1007/s10115-017-1057-x
Crowdsourcing provides access to a pool of human workers who can contribute solutions to tasks that are challenging for computers. Proposals have been made for the use of crowdsourcing in a wide range of data management tasks, including data gatherin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3818::e1fa2081bc918654628462bf836b0159
https://pure.manchester.ac.uk/ws/files/59020827/crowdsourcing.pdf
https://pure.manchester.ac.uk/ws/files/59020827/crowdsourcing.pdf
Autor:
Crescenzi, Valter1 (AUTHOR), Merialdo, Paolo1 (AUTHOR) merialdo@dia.uniroma3.it
Publikováno v:
Applied Artificial Intelligence. Jan/Feb2008, Vol. 22 Issue 1/2, p21-52. 32p. 1 Black and White Photograph, 9 Diagrams, 2 Charts.
Autor:
Bühmann, Lorenz, Usbeck, Ricardo, Ngomo, Axel Cyrille Ngonga, Saleem, Muhammad, Both, Andreas, CRESCENZI, VALTER, MERIALDO, PAOLO, QIU, DISHENG
Only a small fraction of the information on the Web is represented as Linked Data. This lack of coverage is partly due to the paradigms followed so far to extract Linked Data.While converting structured data to RDF is well supported by tools, most ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3668::87c4359e5c0afaff31ebb90b14ded25f
http://springerlink.com/content/0302-9743/copyright/2005/
http://springerlink.com/content/0302-9743/copyright/2005/
Publikováno v:
ACM Transactions on Knowledge Discovery from Data; Sep2019, Vol. 13 Issue 5, p1-43, 43p