Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/88004
Type: Artigo
Title: Provenance-based quality assessment and inference in data-centric workflow executions
Title Alternative: 
Author: Caron, Clément
Amann, Bernd
Constantin, Camelia
Giroux, Patrick
Santanchè, André
Abstract: In this article we present a rule-based quality model for data centric workflows. The goal is to build a tool assisting workflow designers and users in annotating, exploring and improving the quality of data produced by complex media mining workflow executions. Our approach combines an existing fine-grained provenance generation approach [3] with a new quality assessment model for annotating XML fragments with data/application-specific quality values and inferring new values from existing annotations and provenance dependencies. We define the formal semantics using an appropriate fixpoint operator and illustrate how it can be implemented using standard Jena inference rules provided by current semantic web infrastructures.
In this article we present a rule-based quality model for data centric workflows. The goal is to build a tool assisting workflow designers and users in annotating, exploring and improving the quality of data produced by complex media mining workflow execu
Subject: Sistemas de gestão de fluxo de trabalho
Fluxo de trabalho - Processamento de dados
Framework
Web semântica
Recuperação da informação
Inferência (Lógica)
Ontologias (Recuperação da informação)
Country: Alemanha
Editor: Springer
Citation: Lecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). Springer Verlag, v. 8841, n. , p. 130 - 147, 2014.
Rights: fechado
Fechado
Identifier DOI: 10.1007/978-3-662-45563-0_8
Address: https://link.springer.com/chapter/10.1007/978-3-662-45563-0_8
Date Issue: 2014
Appears in Collections:IC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-84909998879.pdf431.89 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.