Search results
Results from the WOW.Com Content Network
OLE 1.0, released in 1990, was an evolution of the original Dynamic Data Exchange (DDE) concept that Microsoft developed for earlier versions of Windows.While DDE was limited to transferring limited amounts of data between two running applications, OLE was capable of maintaining active links between two documents or even embedding one type of document within another.
In databases, brushing and linking is the connection of two or more views of the same data, such that a change to the representation in one view affects the representation in the other. [1] Brushing and linking is also an important technique in interactive visual analysis , a method for performing visual exploration and analysis of large ...
The simplest kind of linking, one-to-one, where both plots show different projections of the same data, and a point in one plot corresponds to exactly one point in the other. When using area plots, brushing any part of an area has the same effect as brushing it all and is equivalent to selecting all cases in the corresponding category.
2 stars: data is available in a structured format, such as Microsoft Excel file format (.xls). 3 stars: data is available in a non-proprietary structured format, such as Comma-separated values (.csv). 4 stars: data follows W3C standards, like using RDF and employing URIs. 5 stars: all of the others, plus links to other Linked Open Data sources.
OLE Object Linking and Embedding allows a Windows application to control another to enable it to format or calculate data. This may take on the form of "embedding" where an application uses another to handle a task that it is more suited to, for example a PowerPoint presentation may be embedded in an Excel spreadsheet or vice versa. [41] [42 ...
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of objects in a set into a configuration of points mapped into an abstract Cartesian space.
The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [ 1 ] [ 3 ] [ 5 ] [ 6 ] [ 7 ] [ 18 ] Rossi et al. [ 5 ] produced an extensive benchmark of the models, but also other surveys produces similar results.
One may recover the surface itself by gluing a topological disk to the ribbon graph along each boundary component. The partition of the surface into vertex disks, edge disks, and face disks given by the ribbon graph and this gluing process is a different but related representation of the embedding called a band decomposition. [5]