Determining the Quality of Product Data Integration View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015-10-28

AUTHORS

Julian Tiedeken , Thomas Bauer , Joachim Herbst , Manfred Reichert

ABSTRACT

To meet customer demands, companies must manage numerous variants and versions of their products. Since product-related data (e.g., requirements’ specifications, geometric models, and source code, or test cases) are usually scattered over a large number of heterogeneous, autonomous information systems, their integration becomes crucial when developing complex products on one hand and aiming at reduced development costs on the other. In general, product data are created in different stages of the product development process. Furthermore, they should be integrated in a complete and consistent way at certain milestones during process development (e.g., prototype construction). Usually, this data integration process is accomplished manually, which is both costly and error prone. Instead semi-automated product data integration is required meeting the data quality requirements of the various stages during product development. In turn, this necessitates a close monitoring of the progress of the data integration process based on proper metrics. Contemporary approaches solely focus on metrics assessing schema integration, while not measuring the quality and progress of data integration. This paper elicits fundamental requirements relevant in this context. Based on them, we develop appropriate metrics for measuring product data quality and apply them in a case study we conducted at an automotive original equipment manufacturer. More... »

PAGES

267-284

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-26148-5_16

DOI

http://dx.doi.org/10.1007/978-3-319-26148-5_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029800659


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