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Gather/scatter is a type of memory addressing that at once collects (gathers) from, or stores (scatters) data to, multiple, arbitrary memory indices. Examples of its use include sparse linear algebra operations, [ 1 ] sorting algorithms, fast Fourier transforms , [ 2 ] and some computational graph theory problems. [ 3 ]
Attributes captured by a CMDB vary based on CI category, and can number up to the hundreds. Some examples include: CI Unique Identifier or Identification Code; CI Name or Label (often, both long names and short names) CI Abbreviations or Acronyms; CI Description; CI Ownership (organizations and people) CI Importance
The terms schema matching and mapping are often used interchangeably for a database process. For this article, we differentiate the two as follows: schema matching is the process of identifying that two objects are semantically related (scope of this article) while mapping refers to the transformations between the objects. For example, in the ...
In computing, vectored I/O, also known as scatter/gather I/O, is a method of input and output by which a single procedure call sequentially reads data from multiple buffers and writes it to a single data stream (gather), or reads data from a data stream and writes it to multiple buffers (scatter), as defined in a vector of buffers.
In this example, predictions for the weather on more distant days change less and less on each subsequent day and tend towards a steady state vector. [5] This vector represents the probabilities of sunny and rainy weather on all days, and is independent of the initial weather. [5] The steady state vector is defined as:
Conceptual schema: describes the semantics of a domain (the scope of the model). For example, it may be a model of the interest area of an organization or of an industry. This consists of entity classes, representing kinds of things of significance in the domain, and relationships assertions about associations between pairs of entity classes.
Data collection system (DCS) is a computer application that facilitates the process of data collection, allowing specific, structured information to be gathered in a systematic fashion, subsequently enabling data analysis to be performed on the information.
This graphical notation supports a subset of the EXPRESS language. One of the advantages of using EXPRESS-G over EXPRESS is that the structure of a data model can be presented in a more understandable manner. A disadvantage of EXPRESS-G is that complex constraints cannot be formally specified. Figure 1 is an example.