#Define hypercube how to#

Consumers checking dimension names between cubes for doing natural joins must make sure to check DIMENSION_UNIQUE_NAME values, not DIMENSION_NAME values. In this case, the DIMENSION_UNIQUE_NAME value is guaranteed to be different. However, in a multiple hypercube scenario, it is possible for two hypercubes to have a dimension of the same name, each of which has different characteristics. For a given configuration, we consider the boundary of the regions of constant spins consisting of the union of all hypercubes that are.

This is because the subcubes are built from the same pool of available dimensions. First, let us define for each nearest-neighbor bond x, y its dual as the d 1-dimensional closed unit hypercube orthogonal to the segment from x to y and bisecting it at its center. In the true multicube system, if there are two rows in the DIMENSIONS rowset for which the DIMENSION_NAME value is the same (and the CUBE_NAME value is different), these two rows represent the same dimension. There is an important distinction between these two systems for OLE DB for OLAP schema rowsets. By contrast, in a multiple-hypercube system, the catalog or the schema has multiple cubes, each with its own set of dimensions. Different subcubes use a subset of these dimensions. noun A network whose nodes have the connectivity of such an object. A hypercube has 2 n corners, each of which is connected to its n nearest adjacent corners by edges that all have the same length. In a true multicube system, dimensions 'exist' in the catalog or the schema. noun Any of a set of objects resulting from the generalization of a two-dimensional square and a three-dimensional cube to n dimensions. If there are dimensions that are not part of any cube, the CUBE_NAME field of the DIMENSIONS rowset is NULL. The DIMENSIONS rowset has one row for each dimension/cube combination. The CUBES rowset lists all the available cubes, whether there is a single hypercube or many multicubes. In the OLE DB for OLAP metadata model, it is possible to browse both hypercube and multicube schemas. Also, there can be dimensions that do not belong to any cube. That is, dimensions are not 'owned' by any one cube rather, they are available to all cubes. In a multicube, a dimension can be part of multiple cubes. In a hypercube, each dimension belongs to one cube only. Hypercubes and multicubes differ in terms of available metadata. In the multicube model, data is segmented into a set of smaller cubes, each of which is composed of a subset of the available dimensions, as shown in the following illustration: All parts of the manifold represented by this hypercube have identical dimensionality. In the hypercube model, as shown in the following illustration, all data appears logically as a single cube. The result of this query shows you the object representation of a hypercube.Multidimensional databases can present their data to an application using two types of cubes: hypercubes and multicubes. The distance between two nodes of graph is defined as the number of edges connecting them in a shortest path.

It is called dimensionless, because in the XBRL universe, the four basic aspects are not considered XBRL dimensions.Įxample - The dimensionless hypercube import module namespace hypercubes = It does not restrict the value of any of these aspects. The simplest hypercube you can imagine is made of four 'standard' aspects: concept, entity, period and unit.

Note: From a practical perspective, it is disputed whether units are considered aspects, or whether they 'stick' to the value. This hypercube potentially contains 2 x 2 x 3 = 12 facts. If there is more, it often indicates an inconsistency in the submission.įor example, if we stick to the USD unit, you can imagine a cube whose width represents concepts (Assets, Revenues), the height of which represents entities (Amex, Disney) and the depth of which represents periods (year 2012, year 2013, year 2014). Usually, there will be either zero or one fact for each possible tuple of aspects. It is possible to organize facts along hypercubes, the dimensions of which are these aspects. HypercubesĬoncepts, entities, periods, units, as well as further dimensions are called aspects. We now go more into detais on this, and eventually show how you can build your own hypercubes and query for fact tables, outside of any SEC network. The facts in an SEC network are actually organized in a hypercubic structure. You may have seen at several place occurrences of the word 'Hypercube'.

#Define hypercube how to#

In the former section, we showed how to get the fact table associated with an SEC network.