Information systems of the enterprise, as a rule, contain applications used by senior managers and designed for complex multidimensional data analysis, their dynamics, trends, etc.
Such an analysis is ultimately intended to facilitate decision-making. Such systems are often called – decision support systems. These applications typically have the means of providing the user with aggregate data for different samples from the original set in a convenient form for perception and analysis.
Most often, these aggregate functions form a multi-dimensional (and therefore non-relational) data set (often called a hypercube or metacube), the axis of which contain the parameters, and cells – contain aggregate data dependent on them. Along each axis the data can be organized in a hierarchy that reflects different levels of their detailed elaboration. Thanks to this data model, users can formulate complex queries, generate reports, and receive data subset.
The technology of integrated multidimensional data analysis is called OLAP (On-Line Analytical Processing). OLAP – is a key component of data warehousing, i.e. collecting, sifting and pre-processing of data in order to provide the resulting information to users for statistical analysis (and often to create analytical reports). OLAP concept was described in 1993 by E.F. Codd, well-known researcher of the database and author of relational data model.
Currently, support for OLAP is implemented in many databases and tools, as this is an optimal solution for a large class of applications where users are faced with multidimensional data (i.e. with data, depending on several parameters, e.g. time, location and other characteristics).
Often, to speed up execution of users’ requests the data of cubes are computed in advance and stored in a multidimensional database.
We should note that the multivariate data analysis may be implemented as in a client application, as in the database server. All leading manufacturers of server DBMS (IBM, Informix, Microsoft, Oracle, and Sybase) produce tools for such an analysis.
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