Advances in Conceptual Modeling - Applications and by Juan Trujillo, Gillian Dobbie, Hannu Kangassalo, Sven

By Juan Trujillo, Gillian Dobbie, Hannu Kangassalo, Sven Hartmann, Markus Kirchberg, Matti Rossi, Iris Reinhartz-Berger, Esteban Zimányi, Flavius Frasincar

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 awarded have been conscientiously reviewed and chosen from eighty two submissions. The papers are equipped in sections at the workshops Semantic and Conceptual concerns in GIS (SeCoGIS); Conceptual Modeling of existence Sciences functions (CMLSA); Conceptual Modelling of providers (CMS); lively Conceptual Modeling of studying (ACM-L); internet details platforms Modeling (WISM); area Engineering (DE@ER); and Foundations and Practices of UML (FP-UML).

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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, ... Applications, incl. Internet Web, and HCI)

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Therefore, spatial and temporal descriptors are mapped prior to properties. The contribution of the Complex Mapping Inference Engine is its ability to compare concepts which structure is more complex than the one used by existing semantic mapping approaches that produce semantic relations. g. 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.

We define the MBR of a geographic object o, M BR(o) = I1 (o)× I2 (o) where Ii (o) = [li , ui ](li , ui ∈ E 1 ) is the minimum interval describing the extent of o along the dimension i. In the same way, we define a rectangle query q = [l1q , uq1 ] × [l2q , uq2 ]. Finally, the range query to find all the objects o having at least one point in common with q is defined as RQ(q) = {o | q ∩ M BR(o) = ∅}. g. g. STR R-tree) variants [11] are the most popular SAMs used to solve range queries in GIS. In Gaede and Gunther [5] these structures are presented in the group of SAMs based on the technique of overlapping regions.

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.

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