ETDs @PUC-Rio
| Título: | AN APPROACH FOR DEALING WITH INCONSISTENCIES IN DATA MASHUPS | ||||||||||||
| Autor: |
EVELINE RUSSO SACRAMENTO FERREIRA |
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| Colaborador(es): |
MARCO ANTONIO CASANOVA - Orientador |
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| Catalogação: | 24/MAI/2016 | Língua(s): | ENGLISH - UNITED STATES |
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| Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
| Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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| Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=26459&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=26459&idi=2 |
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| DOI: | https://doi.org/10.17771/PUCRio.acad.26459 | ||||||||||||
| Resumo: | |||||||||||||
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With the amount of data available on the Web, consumers can mashup
and quickly integrate data from different sources belonging to the same
application domain. However, data mashups constructed from independent and
heterogeneous data sources may contain inconsistencies and, therefore, puzzle the
user when observing the data. This thesis addresses the problem of creating a
consistent data mashup from mutually inconsistent data sources. Specifically, it
deals with the problem of testing, when data to be combined is inconsistent with
respect to a predefined set of constraints. The main contributions of this thesis are:
(1) the formalization of the notion of consistent data mashups by treating the data
returned from the data sources as a default theory and considering a consistent
data mashup as an extension of this theory; (2) a model checker for a family of
Description Logics, which analyzes and separates consistent from inconsistent
data and also tests the consistency and completeness of the obtained data
mashups; (3) a heuristic procedure for computing such consistent data mashups.
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