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Table 1: Data structures and parameters Data structures

in REMINDIN': Semantic Query Routing in Peer-to-Peer Networks Based on Social Metaphors
by Christoph Tempich, Steffen Staab, Adrian Wranik

Table 1: Data structures and parameters Data structures

in REMINDIN': Semantic Query Routing in Peer-to-Peer Networks Based on Social Metaphors
by Christoph Tempich, Steffen Staab, Adrian Wranik

Table 16: Structure of the Data Set.

in An Internally Replicated Quasi-Experimental Comparison of Checklist and Perspective-based Reading of Code Documents
by Oliver Laitenberger, Khaled El Emam, Thomas Harbich

Table 16: Structure of the Data Set.

in Comparison of Checklist and Perspective-based
by Council Canada, Harbich T, Council Canada, Oliver Laitenberger, Khaled El Emam, Thomas Harbich, Oliver Laitenberger, Khaled El Emam, Thomas Harbich 1999

Table 4. Data structuration and data filtering summary.

in LOG DATA PREPARATION FOR MINING WEB USAGE PATTERNS
by G. Castellano, A. M. Fanelli, M. A. Torsello

Table 2: Semantic amp; structural data heterogeneity

in Querying Heterogeneous Land Use Data: Problems and Potential
by N. Wiegand, E. D. Patterson, N. Zhou, S. Ventura, I. F. Cruz
"... In PAGE 4: ... An example is, Where are all the row crop fields in Dane, Racine, and Eau Claire Counties? A query of this kind is relatively straightforward when using one data set but more difficult when posed over a larger geographic area. Table2 illustrates the heterogeneity of attribute ... In PAGE 4: ... There may be multiple data sets covering all or parts of a geographic area, arising from overlapping jurisdictions. For example, regional planning commissions may overlap county data, and cities are nested within counties, as seen in Table2 with Dane County and the city of Madison. At the other extreme, holes may exist in data sets such as Eau Claire County data that excludes the city of Eau Claire.... ..."

Table 2: Semantic amp; structural data heterogeneity

in Querying Heterogeneous Land Use Data: Problems and Potential
by N. Wiegand, E. D. Patterson, N. Zhou, S. Ventura, I. F. Cruz
"... In PAGE 4: ... An example is, Where are all the row crop fields in Dane, Racine, and Eau Claire Counties? A query of this kind is relatively straightforward when using one data set but more difficult when posed over a larger geographic area. Table2 illustrates the heterogeneity of attribute ... In PAGE 4: ... There may be multiple data sets covering all or parts of a geographic area, arising from overlapping jurisdictions. For example, regional planning commissions may overlap county data, and cities are nested within counties, as seen in Table2 with Dane County and the city of Madison. At the other extreme, holes may exist in data sets such as Eau Claire County data that excludes the city of Eau Claire.... ..."

TABLE IV PSNR FOR structures DATA SET.

in Demosaicing using optimal recovery
by D. Darian Muresan, Thomas W. Parks 2005
Cited by 9

Table 5 UML Structured Data Types

in unknown title
by unknown authors

Table 6: An example describing the structure of the data

in COALITION FORMATION AND THE INCLUSION OF GREEN PARTIES IN SWEDISH LOCAL GOVERNMENT*
by Hanna Bäck
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