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

ISBN-10: 364216384X

ISBN-13: 9783642163845

This e-book constitutes the refereed court cases 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 offered 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 purposes (CMLSA); Conceptual Modelling of prone (CMS); energetic Conceptual Modeling of studying (ACM-L); net details structures Modeling (WISM); area Engineering (DE@ER); and Foundations and Practices of UML (FP-UML).

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Additional 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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Such a resolution consists basically of comparing different concepts and measuring the semantic similarity between them. Many researchers have been interested in measuring the semantic similarity between geospatial concepts, and different solutions have been proposed [3], [13], [4], [11], and [10]. Among these solutions, the Geosemantic proximity notion (GsP), proposed by [4], allows to qualitatively evaluate the semantic similarity of geospatial concepts. While the GsP notion can be used to a certain extent to support the interoperability between geospatial datacubes, the efficiency of such interoperability can be improved by extending this notion.

A basic tool in compact data structures is the rank operation: given a sequence S of length N , drawn from an alphabet Σ of size σ, ranka (S, i) counts the occurrences of symbol a ∈ Σ in S[1, i]. For the special case Σ = {0, 1} (S is a bit-vector B), the rank operation can be implemented in constant time and using little additional space on top of B (o(n) in theory [7]). For example, given a bitmap B = 1000110, rank0 (B, 5) = 3 and rank1 (B, 7) = 3. 3 Our Compact Representation Our structure is based on the decomposition of the problem in its d dimensions.

To store these integer coordinates without losing precision we use a compressed storage scheme. An ordered array X = x1 x2 . . xN is represented as a sequence of nonnegative differences between consecutive values yi+1 = xi+1 − xi and y1 = x1 . Let Y = y1 y2 . . yN be this sequence, so that xi = 1≤j≤i yj . Array Y is a representation of X that can be compressed by exploiting the fact that consecutive differences are smaller numbers. These small numbers can be encoded with different coding algorithms.

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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, ... Applications, incl. Internet Web, and HCI) by Juan Trujillo, Gillian Dobbie, Hannu Kangassalo, Sven Hartmann, Markus Kirchberg, Matti Rossi, Iris Reinhartz-Berger, Esteban Zimányi, Flavius Frasincar


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