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Read e-book online Advances in Conceptual Modeling – Applications and PDF

By Jean Brodeur, Esteban Zimányi (auth.), Juan Trujillo, Gillian Dobbie, Hannu Kangassalo, Sven Hartmann, Markus Kirchberg, Matti Rossi, Iris Reinhartz-Berger, Esteban Zimányi, Flavius Frasincar (eds.)

ISBN-10: 364216384X

ISBN-13: 9783642163845

ISBN-10: 3642163858

ISBN-13: 9783642163852

This ebook constitutes the refereed complaints of workshops, held on the twenty ninth foreign convention on Conceptual Modeling, ER 2010, in Vancouver, Canada, in November 2010. The 31 revised complete papers provided have been conscientiously reviewed and chosen from eighty two submissions. The papers are prepared in sections at the workshops Semantic and Conceptual concerns in GIS (SeCoGIS); Conceptual Modeling of existence Sciences functions (CMLSA); Conceptual Modelling of companies (CMS); energetic Conceptual Modeling of studying (ACM-L); internet info platforms Modeling (WISM); area Engineering (DE@ER); and Foundations and Practices of UML (FP-UML).

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Read Online or Download Advances in Conceptual Modeling – Applications and Challenges: ER 2010 Workshops ACM-L, CMLSA, CMS, DE@ER, FP-UML, SeCoGIS, WISM, Vancouver, BC, Canada, November 1-4, 2010. Proceedings PDF

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Extra resources for Advances in Conceptual Modeling – Applications and Challenges: ER 2010 Workshops ACM-L, CMLSA, CMS, DE@ER, FP-UML, SeCoGIS, WISM, Vancouver, BC, Canada, November 1-4, 2010. Proceedings

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Another global ontology of Time (temporal relations and attributes) or a domain-independent global ontology. In the second main step, this lexical relation is transformed into a semantic relation between basic MVAC elements: {equivalence, includes, included in, or disjoint}. Example of such transformation is provided in [18]. The Complex Mapping Inference Engine reuses the semantic relations between basic MVAC elements. 2 Complex Mapping Inference Engine The role of the Complex Mapping Inference Engine is to infer semantic relations between complex MVAC elements (properties, relations, descriptors, views, MVACs, and finally, GWS descriptions), based on the semantic relations between the basic MVAC elements that compose them.

Is-a), whereas we have developed mapping rules that takes as input any kind of relations, by comparing their names and ranges while preserving their structure. Also, G-MAP takes as input properties that are enriched with descriptors, a capacity that does not exist in previous systems. 3 Augmented Mapping Inference Engine The contribution of the Augmented Mapping Inference Engine is to exploit the dependencies to discover missing mappings between MVAC elements. For example, consider two properties depth of watercourse and water level.

The resulting predicates are the same ones as those for the measure element. , hyper-cell, dimension aggregation, and measure function) agents can have a better idea about the semantic similarity of multidimensional concepts, and can make appropriate decisions about resolving the semantic heterogeneity that may occur between the elements of different geospatial datacubes. , used for the same subject of analysis, represented with the same precision and having the same scale). 5 Conclusion Resolution of semantic heterogeneity is considered as a significant challenge for interoperability.

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Advances in Conceptual Modeling – Applications and Challenges: ER 2010 Workshops ACM-L, CMLSA, CMS, DE@ER, FP-UML, SeCoGIS, WISM, Vancouver, BC, Canada, November 1-4, 2010. Proceedings by Jean Brodeur, Esteban Zimányi (auth.), Juan Trujillo, Gillian Dobbie, Hannu Kangassalo, Sven Hartmann, Markus Kirchberg, Matti Rossi, Iris Reinhartz-Berger, Esteban Zimányi, Flavius Frasincar (eds.)


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